diff --git a/.gitignore b/.gitignore
index 86308f8c..61b9dd79 100644
--- a/.gitignore
+++ b/.gitignore
@@ -11,3 +11,4 @@ cmake-build-*
 *.a
 *.so
 data
+utils/__pycache__
\ No newline at end of file
diff --git a/california_housing_price_prediction_with_linear_regression/California_housing_prices_predictions_with_lr_python.ipynb b/california_housing_price_prediction_with_linear_regression/California_housing_prices_predictions_with_lr_python.ipynb
new file mode 100644
index 00000000..a6d6ace3
--- /dev/null
+++ b/california_housing_price_prediction_with_linear_regression/California_housing_prices_predictions_with_lr_python.ipynb
@@ -0,0 +1,1240 @@
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "id": "7ffef0ff",
+   "metadata": {},
+   "source": [
+    "### Predicting  California House Prices with  Linear Regression\n",
+    "\n",
+    "### Objective\n",
+    "* To predict California Housing Prices using the most simple Linear Regression Model and see how it performs.\n",
+    "* To understand the modeling workflow using mlpack.\n",
+    "\n",
+    "### About the Data\n",
+    " This dataset is a modified version of the California Housing dataset available from Luís Torgo's page (University of Porto). Luís Torgo obtained it from the StatLib repository (which is closed now). The dataset may also be downloaded from StatLib mirrors.\n",
+    " \n",
+    " This dataset is also used in a book HandsOn-ML ( a very good book and highly recommended).[ https://www.oreilly.com/library/view/hands-on-machine-learning/9781491962282/].\n",
+    " \n",
+    " The dataset in this directory is almost identical to the original, with two differences:\n",
+    "207 values were randomly removed from the totalbedrooms column, so we can discuss what to do with missing data. An additional categorical attribute called oceanproximity was added, indicating (very roughly) whether each block group is near the ocean, near the Bay area, inland or on an island. This allows discussing what to do with categorical data.\n",
+    "Note that the block groups are called \"districts\" in the Jupyter notebooks, simply because in some contexts the name \"block group\" was confusing.\"\n",
+    "\n",
+    "Lets look at the features of the dataset:\n",
+    "* Longitude : Longitude coordinate of the houses.\n",
+    "* Latitude : Latitude coordinate of the houses.\n",
+    "* Housing Median Age : Average life span of houses.\n",
+    "* Total Rooms : Number of rooms in a location.\n",
+    "* Total Bedrooms : Number of bedroooms in a location.\n",
+    "* Population : Population in that location.\n",
+    "* Median Income : Median Income of households in a location.\n",
+    "* Median House Value : Median House Value in a location.\n",
+    "* Ocean Proximity : Closeness to shore. \n",
+    "\n",
+    "### Approach\n",
+    " Here, we will try to recreate the workflow from the book mentioned above. \n",
+    " * Look at the Big Picture.\n",
+    " * Get the Data.\n",
+    " * Discover and Visualize the data to gain insights.\n",
+    " * Pre-Process the data for the Ml Algorithm.\n",
+    " * Create new features. \n",
+    " * Splitting the data.\n",
+    " * Training the ML model using MLPACK.\n",
+    " * Residuals, Errors and Conclusion."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "3c760992",
+   "metadata": {},
+   "source": [
+    "### Big Picture\n",
+    "\n",
+    "Suppose you work in a Real State Agency as an analyst or Data Scientist and your Boss wants you to predict the housing prices in a certain location. You are provided with a dataset. So, what will be the first thing to do?\n",
+    "\n",
+    "If you are probably jumping right into anaylsing the data and ML Algos, then this is a wrong a step. Its a big \"NO\". \n",
+    " <h5> The first thing is to ask Questions. </h5>\n",
+    " \n",
+    " Questions like :  What will be the predictions used for? Will it be fed into some other system or not? And Many More, just to have concrete goals.\n",
+    " \n",
+    " So, your boss says that they will be using the data to get the predcitions so that the other team can work on some investment strategies.\n",
+    " \n",
+    "So, let's get started."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "fc550b59",
+   "metadata": {},
+   "source": [
+    "<h3> Importing Libraries </h3>"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 22,
+   "id": "a1441566",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import mlpack\n",
+    "import pandas as pd\n",
+    "import numpy as np\n",
+    "import matplotlib.image as mpimg\n",
+    "import matplotlib.pyplot as plt\n",
+    "%matplotlib inline\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "5c33741e",
+   "metadata": {},
+   "source": [
+    "<h3>Get the Data</h3>\n",
+    "\n",
+    "Here, we already have the 'CSV' file, so we will simply just download it. "
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "id": "2a9ceafe",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "!wget -q https://datasets.mlpack.org/examples/housing.csv"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "232b2fd3",
+   "metadata": {},
+   "source": [
+    "<h3>  Discover and Visualize the Data </h3>"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 23,
+   "id": "4f51f1c1",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "dataset = pd.read_csv('housing.csv')"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 24,
+   "id": "79251923",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>longitude</th>\n",
+       "      <th>latitude</th>\n",
+       "      <th>housing_median_age</th>\n",
+       "      <th>total_rooms</th>\n",
+       "      <th>total_bedrooms</th>\n",
+       "      <th>population</th>\n",
+       "      <th>households</th>\n",
+       "      <th>median_income</th>\n",
+       "      <th>median_house_value</th>\n",
+       "      <th>ocean_proximity</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>-122.23</td>\n",
+       "      <td>37.88</td>\n",
+       "      <td>41.0</td>\n",
+       "      <td>880.0</td>\n",
+       "      <td>129.0</td>\n",
+       "      <td>322.0</td>\n",
+       "      <td>126.0</td>\n",
+       "      <td>8.3252</td>\n",
+       "      <td>452600.0</td>\n",
+       "      <td>NEAR BAY</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>-122.22</td>\n",
+       "      <td>37.86</td>\n",
+       "      <td>21.0</td>\n",
+       "      <td>7099.0</td>\n",
+       "      <td>1106.0</td>\n",
+       "      <td>2401.0</td>\n",
+       "      <td>1138.0</td>\n",
+       "      <td>8.3014</td>\n",
+       "      <td>358500.0</td>\n",
+       "      <td>NEAR BAY</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>-122.24</td>\n",
+       "      <td>37.85</td>\n",
+       "      <td>52.0</td>\n",
+       "      <td>1467.0</td>\n",
+       "      <td>190.0</td>\n",
+       "      <td>496.0</td>\n",
+       "      <td>177.0</td>\n",
+       "      <td>7.2574</td>\n",
+       "      <td>352100.0</td>\n",
+       "      <td>NEAR BAY</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>-122.25</td>\n",
+       "      <td>37.85</td>\n",
+       "      <td>52.0</td>\n",
+       "      <td>1274.0</td>\n",
+       "      <td>235.0</td>\n",
+       "      <td>558.0</td>\n",
+       "      <td>219.0</td>\n",
+       "      <td>5.6431</td>\n",
+       "      <td>341300.0</td>\n",
+       "      <td>NEAR BAY</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4</th>\n",
+       "      <td>-122.25</td>\n",
+       "      <td>37.85</td>\n",
+       "      <td>52.0</td>\n",
+       "      <td>1627.0</td>\n",
+       "      <td>280.0</td>\n",
+       "      <td>565.0</td>\n",
+       "      <td>259.0</td>\n",
+       "      <td>3.8462</td>\n",
+       "      <td>342200.0</td>\n",
+       "      <td>NEAR BAY</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "   longitude  latitude  housing_median_age  total_rooms  total_bedrooms  \\\n",
+       "0    -122.23     37.88                41.0        880.0           129.0   \n",
+       "1    -122.22     37.86                21.0       7099.0          1106.0   \n",
+       "2    -122.24     37.85                52.0       1467.0           190.0   \n",
+       "3    -122.25     37.85                52.0       1274.0           235.0   \n",
+       "4    -122.25     37.85                52.0       1627.0           280.0   \n",
+       "\n",
+       "   population  households  median_income  median_house_value ocean_proximity  \n",
+       "0       322.0       126.0         8.3252            452600.0        NEAR BAY  \n",
+       "1      2401.0      1138.0         8.3014            358500.0        NEAR BAY  \n",
+       "2       496.0       177.0         7.2574            352100.0        NEAR BAY  \n",
+       "3       558.0       219.0         5.6431            341300.0        NEAR BAY  \n",
+       "4       565.0       259.0         3.8462            342200.0        NEAR BAY  "
+      ]
+     },
+     "execution_count": 24,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "# Lets print the first 5 rows of the dataset.\n",
+    "dataset.head() "
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 25,
+   "id": "ae042e5d",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>longitude</th>\n",
+       "      <th>latitude</th>\n",
+       "      <th>housing_median_age</th>\n",
+       "      <th>total_rooms</th>\n",
+       "      <th>total_bedrooms</th>\n",
+       "      <th>population</th>\n",
+       "      <th>households</th>\n",
+       "      <th>median_income</th>\n",
+       "      <th>median_house_value</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>count</th>\n",
+       "      <td>20640.000000</td>\n",
+       "      <td>20640.000000</td>\n",
+       "      <td>20640.000000</td>\n",
+       "      <td>20640.000000</td>\n",
+       "      <td>20433.000000</td>\n",
+       "      <td>20640.000000</td>\n",
+       "      <td>20640.000000</td>\n",
+       "      <td>20640.000000</td>\n",
+       "      <td>20640.000000</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>mean</th>\n",
+       "      <td>-119.569704</td>\n",
+       "      <td>35.631861</td>\n",
+       "      <td>28.639486</td>\n",
+       "      <td>2635.763081</td>\n",
+       "      <td>537.870553</td>\n",
+       "      <td>1425.476744</td>\n",
+       "      <td>499.539680</td>\n",
+       "      <td>3.870671</td>\n",
+       "      <td>206855.816909</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>std</th>\n",
+       "      <td>2.003532</td>\n",
+       "      <td>2.135952</td>\n",
+       "      <td>12.585558</td>\n",
+       "      <td>2181.615252</td>\n",
+       "      <td>421.385070</td>\n",
+       "      <td>1132.462122</td>\n",
+       "      <td>382.329753</td>\n",
+       "      <td>1.899822</td>\n",
+       "      <td>115395.615874</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>min</th>\n",
+       "      <td>-124.350000</td>\n",
+       "      <td>32.540000</td>\n",
+       "      <td>1.000000</td>\n",
+       "      <td>2.000000</td>\n",
+       "      <td>1.000000</td>\n",
+       "      <td>3.000000</td>\n",
+       "      <td>1.000000</td>\n",
+       "      <td>0.499900</td>\n",
+       "      <td>14999.000000</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>25%</th>\n",
+       "      <td>-121.800000</td>\n",
+       "      <td>33.930000</td>\n",
+       "      <td>18.000000</td>\n",
+       "      <td>1447.750000</td>\n",
+       "      <td>296.000000</td>\n",
+       "      <td>787.000000</td>\n",
+       "      <td>280.000000</td>\n",
+       "      <td>2.563400</td>\n",
+       "      <td>119600.000000</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>50%</th>\n",
+       "      <td>-118.490000</td>\n",
+       "      <td>34.260000</td>\n",
+       "      <td>29.000000</td>\n",
+       "      <td>2127.000000</td>\n",
+       "      <td>435.000000</td>\n",
+       "      <td>1166.000000</td>\n",
+       "      <td>409.000000</td>\n",
+       "      <td>3.534800</td>\n",
+       "      <td>179700.000000</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>75%</th>\n",
+       "      <td>-118.010000</td>\n",
+       "      <td>37.710000</td>\n",
+       "      <td>37.000000</td>\n",
+       "      <td>3148.000000</td>\n",
+       "      <td>647.000000</td>\n",
+       "      <td>1725.000000</td>\n",
+       "      <td>605.000000</td>\n",
+       "      <td>4.743250</td>\n",
+       "      <td>264725.000000</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>max</th>\n",
+       "      <td>-114.310000</td>\n",
+       "      <td>41.950000</td>\n",
+       "      <td>52.000000</td>\n",
+       "      <td>39320.000000</td>\n",
+       "      <td>6445.000000</td>\n",
+       "      <td>35682.000000</td>\n",
+       "      <td>6082.000000</td>\n",
+       "      <td>15.000100</td>\n",
+       "      <td>500001.000000</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "          longitude      latitude  housing_median_age   total_rooms  \\\n",
+       "count  20640.000000  20640.000000        20640.000000  20640.000000   \n",
+       "mean    -119.569704     35.631861           28.639486   2635.763081   \n",
+       "std        2.003532      2.135952           12.585558   2181.615252   \n",
+       "min     -124.350000     32.540000            1.000000      2.000000   \n",
+       "25%     -121.800000     33.930000           18.000000   1447.750000   \n",
+       "50%     -118.490000     34.260000           29.000000   2127.000000   \n",
+       "75%     -118.010000     37.710000           37.000000   3148.000000   \n",
+       "max     -114.310000     41.950000           52.000000  39320.000000   \n",
+       "\n",
+       "       total_bedrooms    population    households  median_income  \\\n",
+       "count    20433.000000  20640.000000  20640.000000   20640.000000   \n",
+       "mean       537.870553   1425.476744    499.539680       3.870671   \n",
+       "std        421.385070   1132.462122    382.329753       1.899822   \n",
+       "min          1.000000      3.000000      1.000000       0.499900   \n",
+       "25%        296.000000    787.000000    280.000000       2.563400   \n",
+       "50%        435.000000   1166.000000    409.000000       3.534800   \n",
+       "75%        647.000000   1725.000000    605.000000       4.743250   \n",
+       "max       6445.000000  35682.000000   6082.000000      15.000100   \n",
+       "\n",
+       "       median_house_value  \n",
+       "count        20640.000000  \n",
+       "mean        206855.816909  \n",
+       "std         115395.615874  \n",
+       "min          14999.000000  \n",
+       "25%         119600.000000  \n",
+       "50%         179700.000000  \n",
+       "75%         264725.000000  \n",
+       "max         500001.000000  "
+      ]
+     },
+     "execution_count": 25,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "# Lets look into some statistics.\n",
+    "dataset.describe()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 26,
+   "id": "cfcea99e",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "<class 'pandas.core.frame.DataFrame'>\n",
+      "RangeIndex: 20640 entries, 0 to 20639\n",
+      "Data columns (total 10 columns):\n",
+      " #   Column              Non-Null Count  Dtype  \n",
+      "---  ------              --------------  -----  \n",
+      " 0   longitude           20640 non-null  float64\n",
+      " 1   latitude            20640 non-null  float64\n",
+      " 2   housing_median_age  20640 non-null  float64\n",
+      " 3   total_rooms         20640 non-null  float64\n",
+      " 4   total_bedrooms      20433 non-null  float64\n",
+      " 5   population          20640 non-null  float64\n",
+      " 6   households          20640 non-null  float64\n",
+      " 7   median_income       20640 non-null  float64\n",
+      " 8   median_house_value  20640 non-null  float64\n",
+      " 9   ocean_proximity     20640 non-null  object \n",
+      "dtypes: float64(9), object(1)\n",
+      "memory usage: 1.6+ MB\n"
+     ]
+    }
+   ],
+   "source": [
+    "dataset.info()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "57d17c78",
+   "metadata": {},
+   "source": [
+    "<i>If you look closely, \"total_bedrooms\" column has some missing values. Later, we will learn how to deal with these missing values.</i>"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 27,
+   "id": "015161cf",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 1440x1080 with 9 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "# We are using matplotlib for visualization.\n",
+    "dataset.hist(bins=50, figsize=(20,15))\n",
+    "plt.show()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 28,
+   "id": "607214a7",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "<matplotlib.axes._subplots.AxesSubplot at 0x7fe73ec53790>"
+      ]
+     },
+     "execution_count": 28,
+     "metadata": {},
+     "output_type": "execute_result"
+    },
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 432x288 with 1 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "# Let's plot  a scatter plot according to co-ordinates.\n",
+    "dataset.plot(kind=\"scatter\",\n",
+    "             x=\"longitude\", \n",
+    "             y=\"latitude\", \n",
+    "             alpha = 0.1)\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 29,
+   "id": "a2e78140",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "<matplotlib.legend.Legend at 0x7fe73ec262d0>"
+      ]
+     },
+     "execution_count": 29,
+     "metadata": {},
+     "output_type": "execute_result"
+    },
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 720x504 with 2 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "# Now lets plot according to price.\n",
+    "dataset.plot(kind=\"scatter\",\n",
+    "             x=\"longitude\", \n",
+    "             y=\"latitude\",\n",
+    "             alpha=0.4,\n",
+    "             s=dataset[\"population\"]/100, \n",
+    "             label=\"population\", \n",
+    "             figsize=(10,7),\n",
+    "             c=\"median_house_value\",\n",
+    "             cmap=plt.get_cmap(\"jet\"),\n",
+    "             colorbar=True,\n",
+    "             sharex=False)\n",
+    "plt.legend()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 30,
+   "id": "343a7b40",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 720x504 with 1 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "# Lets plot this on top of a piece of california map.\n",
+    "california_img = mpimg.imread('california.png') #path to california image.\n",
+    "ax = dataset.plot(kind=\"scatter\",\n",
+    "                  x=\"longitude\",\n",
+    "                  y=\"latitude\", \n",
+    "                  figsize=(10,7),\n",
+    "                  s=dataset['population']/100, \n",
+    "                  label=\"Population\",\n",
+    "                  c=\"median_house_value\", \n",
+    "                  cmap=plt.get_cmap(\"jet\"),\n",
+    "                  colorbar=False, alpha=0.4)\n",
+    "plt.imshow(california_img, extent=[-124.55, -113.80, 32.45, 42.05], \n",
+    "           alpha=0.5,\n",
+    "           cmap=plt.get_cmap(\"jet\"))\n",
+    "plt.ylabel(\"Latitude\", fontsize=14)\n",
+    "plt.xlabel(\"Longitude\", fontsize=14)\n",
+    "plt.show()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "6859d178",
+   "metadata": {},
+   "source": [
+    "<h4>  Let's deal with Missing Values</h4>"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 31,
+   "id": "cd7b0f63",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>longitude</th>\n",
+       "      <th>latitude</th>\n",
+       "      <th>housing_median_age</th>\n",
+       "      <th>total_rooms</th>\n",
+       "      <th>total_bedrooms</th>\n",
+       "      <th>population</th>\n",
+       "      <th>households</th>\n",
+       "      <th>median_income</th>\n",
+       "      <th>median_house_value</th>\n",
+       "      <th>ocean_proximity</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>290</th>\n",
+       "      <td>-122.16</td>\n",
+       "      <td>37.77</td>\n",
+       "      <td>47.0</td>\n",
+       "      <td>1256.0</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>570.0</td>\n",
+       "      <td>218.0</td>\n",
+       "      <td>4.3750</td>\n",
+       "      <td>161900.0</td>\n",
+       "      <td>NEAR BAY</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>341</th>\n",
+       "      <td>-122.17</td>\n",
+       "      <td>37.75</td>\n",
+       "      <td>38.0</td>\n",
+       "      <td>992.0</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>732.0</td>\n",
+       "      <td>259.0</td>\n",
+       "      <td>1.6196</td>\n",
+       "      <td>85100.0</td>\n",
+       "      <td>NEAR BAY</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>538</th>\n",
+       "      <td>-122.28</td>\n",
+       "      <td>37.78</td>\n",
+       "      <td>29.0</td>\n",
+       "      <td>5154.0</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>3741.0</td>\n",
+       "      <td>1273.0</td>\n",
+       "      <td>2.5762</td>\n",
+       "      <td>173400.0</td>\n",
+       "      <td>NEAR BAY</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>563</th>\n",
+       "      <td>-122.24</td>\n",
+       "      <td>37.75</td>\n",
+       "      <td>45.0</td>\n",
+       "      <td>891.0</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>384.0</td>\n",
+       "      <td>146.0</td>\n",
+       "      <td>4.9489</td>\n",
+       "      <td>247100.0</td>\n",
+       "      <td>NEAR BAY</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>696</th>\n",
+       "      <td>-122.10</td>\n",
+       "      <td>37.69</td>\n",
+       "      <td>41.0</td>\n",
+       "      <td>746.0</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>387.0</td>\n",
+       "      <td>161.0</td>\n",
+       "      <td>3.9063</td>\n",
+       "      <td>178400.0</td>\n",
+       "      <td>NEAR BAY</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "     longitude  latitude  housing_median_age  total_rooms  total_bedrooms  \\\n",
+       "290    -122.16     37.77                47.0       1256.0             NaN   \n",
+       "341    -122.17     37.75                38.0        992.0             NaN   \n",
+       "538    -122.28     37.78                29.0       5154.0             NaN   \n",
+       "563    -122.24     37.75                45.0        891.0             NaN   \n",
+       "696    -122.10     37.69                41.0        746.0             NaN   \n",
+       "\n",
+       "     population  households  median_income  median_house_value ocean_proximity  \n",
+       "290       570.0       218.0         4.3750            161900.0        NEAR BAY  \n",
+       "341       732.0       259.0         1.6196             85100.0        NEAR BAY  \n",
+       "538      3741.0      1273.0         2.5762            173400.0        NEAR BAY  \n",
+       "563       384.0       146.0         4.9489            247100.0        NEAR BAY  \n",
+       "696       387.0       161.0         3.9063            178400.0        NEAR BAY  "
+      ]
+     },
+     "execution_count": 31,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "# Lets print whichever column has missing values.\n",
+    "sample_incomplete_rows = dataset[dataset.isnull().any(axis=1)].head()\n",
+    "sample_incomplete_rows"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "296cfb38",
+   "metadata": {},
+   "source": [
+    "<i>It can be clearly seen that only total_bedrooms has missing values. Let's fill these missing values using median of the column.</i>\n",
+    "<br>\n",
+    "<b> NOTE: </b>\n",
+    " 1. We can also impute here using mean.\n",
+    " 2. For categorical data, use mode."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 32,
+   "id": "2c182111",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "median = dataset[\"total_bedrooms\"].median() # Here, we used median() method to fill missing values with median of column.\n",
+    "dataset[\"total_bedrooms\"].fillna(median, inplace=True)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 16,
+   "id": "bc5c802e",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "<class 'pandas.core.frame.DataFrame'>\n",
+      "RangeIndex: 20640 entries, 0 to 20639\n",
+      "Data columns (total 10 columns):\n",
+      " #   Column              Non-Null Count  Dtype  \n",
+      "---  ------              --------------  -----  \n",
+      " 0   longitude           20640 non-null  float64\n",
+      " 1   latitude            20640 non-null  float64\n",
+      " 2   housing_median_age  20640 non-null  float64\n",
+      " 3   total_rooms         20640 non-null  float64\n",
+      " 4   total_bedrooms      20640 non-null  float64\n",
+      " 5   population          20640 non-null  float64\n",
+      " 6   households          20640 non-null  float64\n",
+      " 7   median_income       20640 non-null  float64\n",
+      " 8   median_house_value  20640 non-null  float64\n",
+      " 9   ocean_proximity     20640 non-null  object \n",
+      "dtypes: float64(9), object(1)\n",
+      "memory usage: 1.6+ MB\n"
+     ]
+    }
+   ],
+   "source": [
+    "dataset.info()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "5a7562d4",
+   "metadata": {},
+   "source": [
+    "It can be clearly seen now that we have filled all the missing values."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "5241d10d",
+   "metadata": {},
+   "source": [
+    "<h4>Let's deal with Categorical Values</h4>\n",
+    "We will use One hot encoding for this. It will create seperate columns of all categorical features and use : <br>\n",
+    "'1' : If a row has that feature.<br>\n",
+    "'0' : If a row doesn't have that feature."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 33,
+   "id": "c25339f1",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "def one_hot_encoding(data, dimensions, drop= False):\n",
+    "  for dim in dimensions:\n",
+    "    if(type(data.iloc[:,dim].values[0]) == str):\n",
+    "      uniq = data.iloc[:,dim].unique()\n",
+    "      for val in uniq:\n",
+    "        data[f\"{data.columns[dim]}_{val}\"] = data.iloc[:,dim].apply(lambda x: 1 if x == val else 0)\n",
+    "      \n",
+    "  if drop:\n",
+    "    data.drop(data.columns[dimensions], axis=1, inplace=True)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 34,
+   "id": "b38ebafa",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "one_hot_encoding(data=dataset, dimensions=[9],drop=True)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 35,
+   "id": "33f33aad",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "<class 'pandas.core.frame.DataFrame'>\n",
+      "RangeIndex: 20640 entries, 0 to 20639\n",
+      "Data columns (total 14 columns):\n",
+      " #   Column                      Non-Null Count  Dtype  \n",
+      "---  ------                      --------------  -----  \n",
+      " 0   longitude                   20640 non-null  float64\n",
+      " 1   latitude                    20640 non-null  float64\n",
+      " 2   housing_median_age          20640 non-null  float64\n",
+      " 3   total_rooms                 20640 non-null  float64\n",
+      " 4   total_bedrooms              20640 non-null  float64\n",
+      " 5   population                  20640 non-null  float64\n",
+      " 6   households                  20640 non-null  float64\n",
+      " 7   median_income               20640 non-null  float64\n",
+      " 8   median_house_value          20640 non-null  float64\n",
+      " 9   ocean_proximity_NEAR BAY    20640 non-null  int64  \n",
+      " 10  ocean_proximity_<1H OCEAN   20640 non-null  int64  \n",
+      " 11  ocean_proximity_INLAND      20640 non-null  int64  \n",
+      " 12  ocean_proximity_NEAR OCEAN  20640 non-null  int64  \n",
+      " 13  ocean_proximity_ISLAND      20640 non-null  int64  \n",
+      "dtypes: float64(9), int64(5)\n",
+      "memory usage: 2.2 MB\n"
+     ]
+    }
+   ],
+   "source": [
+    "dataset.info()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "f7cd5c17",
+   "metadata": {},
+   "source": [
+    "<i> As discussed, it created four different features according to categorical values. </i> <br>\n",
+    "<h4> Note : </h4>\n",
+    "   Make sure to remove original Categorical Column as our algorithm works with numeical values."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "1a614e50",
+   "metadata": {},
+   "source": [
+    "<h3>Let's create some more features </h3>\n",
+    "\n",
+    "If you study the dataset, the rooms and bedrooms data corresponds to the whole location. Since we are trying to predict house price, let's create some features for it.\n",
+    "* Rooms per Household : To get an approximate no. of rooms each house has.\n",
+    "* Bedrooms per Room : To get an approximate no. of bedrooms among total rooms.\n",
+    "* Population per household : To get an approximate no. of residents in a house."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 36,
+   "id": "3c3adf00",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "dataset[\"rooms_per_household\"] = dataset[\"total_rooms\"]/dataset[\"households\"]\n",
+    "dataset[\"bedrooms_per_room\"] = dataset[\"total_bedrooms\"]/dataset[\"total_rooms\"]\n",
+    "dataset[\"population_per_household\"] = dataset[\"population\"]/dataset[\"households\"]"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 37,
+   "id": "4130bd66",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 1080x720 with 2 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "import seaborn as sns\n",
+    "plt.figure(figsize=(15,10))\n",
+    "sns.heatmap(dataset.corr(),annot=True)\n",
+    "plt.show()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "493fde97",
+   "metadata": {},
+   "source": [
+    "<h4> It is important to check what kind of correlation the features has, especially during feature engineering. </h4> "
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "7b506260",
+   "metadata": {},
+   "source": [
+    "<h3> Let's prepare the data for Training </h3>"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 38,
+   "id": "7816ee97",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "#This function will split the data into requirement ratio and shuffle so that we can as get random data.\n",
+    "def splitTrainTest(data, testRatio):\n",
+    "  shuffledIndices = np.random.permutation(len(data))\n",
+    "  testSetSize = int(len(data)*testRatio)\n",
+    "  testIndices = shuffledIndices[:testSetSize]\n",
+    "  trainIndices = shuffledIndices[testSetSize:]\n",
+    "  return data.iloc[trainIndices], data.iloc[testIndices]\n",
+    "\n",
+    "trainSet, testSet = splitTrainTest(dataset, 0.2)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 39,
+   "id": "18738ea5",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "trainLabels= trainSet[\"median_house_value\"]\n",
+    "trainSet = trainSet.drop(\"median_house_value\", axis = 1)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 40,
+   "id": "0e289618",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "testLabels = testSet[\"median_house_value\"]\n",
+    "testSet = testSet.drop(\"median_house_value\", axis = 1)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "3deafa5e",
+   "metadata": {},
+   "source": [
+    "<h3>Model Training </h3>\n",
+    "At this point, now we have our data processed and are ready to create a model for predcition."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 41,
+   "id": "cb5381fa",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "output = mlpack.linear_regression(training=trainSet, training_responses=trainLabels, verbose=True)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 42,
+   "id": "670501f9",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "model = output[\"output_model\"]"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "34596fba",
+   "metadata": {},
+   "source": [
+    "<i> Our Model is Trained, now lets make predictions on the test_set</i>"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 43,
+   "id": "a860072b",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "predictions = mlpack.linear_regression(input_model=model, test=testSet)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 44,
+   "id": "15a9ef75",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "yPreds = predictions[\"output_predictions\"].reshape(-1,1).squeeze()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "1c5f2eaa",
+   "metadata": {},
+   "source": [
+    "<i> Let's see the residuals now</i>"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 46,
+   "id": "b3a19cd7",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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MqqvRxHEZcBZwLrC2vHDMzKzqGk0cayLi+6VGYmZmg0Kjl+P+UtJRkpokje94lBqZmZlVUqM1jtnp7+dyZQFs17fhmHW2atUqWltb1z9va2sj1sUARmRmDSWOiJhWdiBm9bS2tjLnzHmMbWoGoL31ZsZtP31ggzIb5hpKHJL+vV55RPy4m33OAw4ElkfErqnsJOAjwIq02QkRcXVadzxwBFnn+9ERcU2Dr8GGuLFNzYyfuiMAK9sXD2gstdatXUNbW1unst12241Ro0YNUERm5Wu0qWrP3PJoYB/gDqDLxAGcD5xRZ5tvRsRp+QJJOwOzgF2ArYHrJL0qInwFl1XaM8uXcNrVzzOxbTWQJbYz58Aee+wxsIGZlajRpqpP5p9LGgf8pId9bpLU3GAcM4FLIuIF4EFJi4AZwC0N7m82YMZM2nZ9jchsOOjtsOr/AHbo5b6fkHSPpPMkbZnKJgOP5LZZkspeRNKRkuZLmr9ixYp6m5iZWYkaHVb9l5LmpcevgIXAlb043/eB7YHpQDvwjY5T1Nm27qUzEXF2RLRERMuECRN6EYKZmW2MRvs48n0Sa4CHImJJ0ZNFxLKOZUnnAFelp0uAKblNtwGWFj2+mZmVr6EaRxrs8M9kI+RuCazqzckkNeWeHgTcm5bnAbMkbSppGlkz2K29OYeZmZWr0ctx3wecCtxI1qz0XUmfi4ifdbPPxcDewFaSlgBfBvaWNJ2sGWox8FGAiFggaS5wH1mNZo6vqDIzq6ZGm6pOBPaMiOUAkiYA1wFdJo6IeH+d4h92s/3JwMkNxmNmZgOk0auqXtKRNJLHC+xrZmZDSKM1jl9Luga4OD0/BLi6nJDMzKzKeppz/JXApIj4nKR3A28i6+O4BbiwH+IzM7OK6am56VvA0wAR8fOIOCYiPkNW2/hW2cGZmVn19JQ4miPintrCiJhPNo2smZkNMz0ljtHdrHtpXwZiZmaDQ0+d47dJ+khEnJMvlHQEcHt5YZkNTh5m3YaDnhLHp4ErJB3KhkTRAowiu/PbrE8N9hn/PMy6DQfdJo40ttQbJb0Z2DUV/yoiflt6ZDYsDYUZ/zzMug11jc7HcQNwQ8mxmAHVnvHPzHz3t5mZFeTEYWZmhThxmJlZIU4cZmZWiBOHmZkV4sRhZmaFOHGYmVkhThxmZlaIE4eZmRXS6AyAZtYLHvTQhiInDrMSedBDG4qcOMxK5kEPbahxH4eZmRXixGFmZoU4cZiZWSFOHGZmVkhpiUPSeZKWS7o3VzZe0rWS7k9/t8ytO17SIkkLJe1XVlxmZrZxyqxxnA/sX1N2HHB9ROwAXJ+eI2lnYBawS9rne5JGlBibmZn1UmmJIyJuAp6oKZ4JXJCWLwDelSu/JCJeiIgHgUXAjLJiMzOz3uvv+zgmRUQ7QES0S5qYyicDf8xttySVmQ1pq1atorW1tVOZ7yy3qqvKDYCqUxZ1N5SOBI4E2HbbbcuMyfpB7RdnW1sbsa7uP/2Q1Nraypwz5zG2qRnwneU2OPR34lgmqSnVNpqA5al8CTAlt902wNJ6B4iIs4GzAVpaWobPN8wQVfvF2d56M+O2nz6wQfWzsU3NvrPcBpX+vhx3HjA7Lc8GrsyVz5K0qaRpwA7Arf0cmw2Qji/O8VN3ZLOtth7ocMysB6XVOCRdDOwNbCVpCfBl4BRgrqQjgIeBgwEiYoGkucB9wBpgTkSsLSs2MzPrvdISR0S8v4tV+3Sx/cnAyWXFY1YFtcOsD7c+HRsaqtI5bjYs1A6zPhz7dGzwc+Iw62f5YdZXti8e0FjMesNjVZmZWSFOHGZmVogTh5mZFeI+DutXw/1OcbOhwInD+pXvFDcb/Jw4rN/lh9jwVUWd1d7nAR700KrHicOsQmrv8/Cgh1ZFThxmFZO/z8OsinxVlZmZFeLEYWZmhThxmJlZIU4cZmZWiBOHmZkV4sRhZmaFOHGYmVkhThxmZlaIE4eZmRXixGFmZoV4yBGzCqsd9NADHloVOHGYVVh+0EMPeGhV4cRhVnEe9NCqxn0cZmZWiGscVipPFWs29DhxWKk8VazZ0OPEYaXzVLFmQ8uAJA5Ji4GngbXAmohokTQeuBRoBhYD74uIJwciPjMz69pAdo6/OSKmR0RLen4ccH1E7ABcn56bmVnFVOmqqpnABWn5AuBdAxiLmZl1YaASRwC/kXS7pCNT2aSIaAdIfyfW21HSkZLmS5q/YsWKfgrXzMw6DFTn+F4RsVTSROBaSX9udMeIOBs4G6ClpcXXddqwUTv8CHgIEhsYA5I4ImJp+rtc0hXADGCZpKaIaJfUBCwfiNjMqio//AjgIUhswPR7U5WkzSRt3rEMvA24F5gHzE6bzQau7O/YzKquY/iR8VN3XH9vjFl/G4gaxyTgCkkd578oIn4t6TZgrqQjgIeBgwcgNrNBw01XNlD6PXFExAPAa+uUPw7s09/xmA1WbrqygeI7x80GMY+cawOhSvdxmJnZIODEYWZmhbipymyIqh3SHtx5bn3DicP6XP4Ly/NvDJzaIe3deW59xYnD+lz+C8vzbwys/JD2Zn3FicM2Wr1Z/jafNJXxU3f0/BtmQ5ATh200z/I3OLkPxHrLicP6hGf5q77aO83b2to44/r7Gbt1M/DiPhAnFuuKE4fZMFF7p3lHzbCrPhB3rltXnDjMhpH8neaN1AzduW71+AZAMzMrxInDzMwKceIwM7NC3MdhNkTUu2qqyF37G7u/DR9OHGZDRFdXTZW1vy/XHb6cOMyGkKJXTRXZv+h9IDZ0OXGYWUN6ug/EU9kOH04cVli9sancFj48dFcj8VS2w4cTh/WoXqLIN1F4bCrrkE8stTWQ1auzhDJy5Mj1Za6RDE5OHNajrgYx9NhU1p16TVsjxoxn4rQNnxvXSAYnJw5riAcxtN6obdraZNzELocw8VVag4cTh5lVQm3N9qklf+Xot7ax0047rd/GiaQanDjMrDJqa7anXd3qzvYKcuIYBoo2AfiqKauKfFOXVYcTxzBQdF4Fz+hn/aHoECdF7xNxn0l5nDiGoO7mAO/N9u4MtzIUHeKkdvvaPpDay319Z3t5Kpc4JO0PfBsYAZwbEacMcEiVV/Q+i56GjnANw/pL0SFSarfP94HUXu7b3Z3ttUlmY59DubWZqtWeKpU4JI0AzgTeCiwBbpM0LyLu6+tz1f5D9Oc/Ql9/CIreZ9HT0BGuYdhg0d3lvt197uslmY15XvQKsJ6+A3r6MTjQtadKJQ5gBrAoIh4AkHQJMBPo88TR2trKYV/8DpuNfwXPPvE3TvjAWzv9o5epra2N/77oWjYb/wqAjT5/bbsvwDPLHuaJl70sO/5jSxnx/POdn48ZX2z7Xj7vy2MN9+dVimXQPq/53PeV555cxknnP8CWr8i+7Hv6P93Td0Dt+sceXMC4qbswtpToi1NEda6WkfReYP+I+I/0/DDgdRHxidw2RwJHpqevBhb2wam3Ah7rg+P0F8dbLsdbLsdbrkbinRoRE3p7gqrVOFSnrFNmi4izgbP79KTS/Iho6ctjlsnxlsvxlsvxlqs/4q3a1LFLgCm559sASwcoFjMzq6NqieM2YAdJ0ySNAmYB8wY4JjMzy6lUU1VErJH0CeAasstxz4uIBf1w6j5t+uoHjrdcjrdcjrdcpcdbqc5xMzOrvqo1VZmZWcU5cZiZWSFDJnFI+oqkeyTdJek3krbOrTte0iJJCyXtlyvfQ1JrWvcdSUrlm0q6NJX/SVJzbp/Zku5Pj9m58mlp2/vTvt3eBi7pVEl/TjFfIWmLisd7sKQFktZJaqlZV7l4e0vS/ul1LJJ0XBnnyJ3rPEnLJd2bKxsv6dr0Oq+VtGVuXenvczexTpF0g6S29Dn4VMXjHS3pVkl3p3j/s8rx5vYbIelOSVdVOt6IGBIPYGxu+WjgrLS8M3A3sCkwDfgrMCKtuxV4A9n9I/8LvD2VH5XbfxZwaVoeDzyQ/m6ZlrdM6+YCs9LyWcDHe4j3bcAmaflrwNcqHu9OZDdc3gi05MorGW8vP0MjUvzbAaPS69q5xM/svwD/BNybK/s6cFxaPq6/PxfdxNoE/FNa3hz4S4qpqvEKGJOWRwJ/Al5f1XhzcR8DXARcVenPQ1n/KQbyARwPfD+3fHxu3TXpTW0C/pwrfz/wg/w2aXkTsrswld8mrftBKlPapiMRvAG4pkC8BwEXDoZ4eXHiqHS8BT83nY5b+9pK+qw20zlxLASa0nITsLC/3ueCcV9JNqZc5eMFXgbcAbyuyvGS3bd2PfAWNiSOSsY7ZJqqACSdLOkR4FDgS6l4MvBIbrMlqWxyWq4t77RPRKwB/g68vJtjvRx4Km1be6xGfJjsl8FgiTdvsMXbna7O358mRUQ7QPo7sYfY+vJ9bkhq4tid7Fd8ZeNNzT53AcuBayOi0vEC3wI+D6zLlVUy3kGVOCRdJ+neOo+ZABFxYkRMAS4EOsa36moYk+6GNym6T93ynuJNr+lEYE2KufLx1jFg8XYTU2/113l6oz/e556DkMYAlwOfjoiV3W3ai3P3abwRsTYippP9kp8hadduNh/QeCUdCCyPiNu72y6/Sy/O3WfxVuoGwJ5ExL4NbnoR8Cvgy3Q9jMmStFxbTm6fJZI2AcYBT6TyvWv2uZGsyreFpE1SJt8GWBoR+9GN1Al1ILBPpDpilePtwoDF24tYe1KFIW+WSWqKiHZJTWS/lruLrS/f525JGkmWNC6MiJ9XPd4OEfGUpBuB/Ssc717AOyUdAIwGxkr6aWXjLdKuWeUHsENu+ZPAz9LyLnTuRHqADZ1It5F1mHV0Ih2QyufQuRNpbloeDzxI1oG0ZVoen9ZdRufO26N6iHd/suHiJ9SUVzLeXHw30rmPo9LxFvwMbZLin8aGzvFdSv7cNtO5j+NUOneGfr0/3+du4hTwY+BbNeVVjXcCsEVafinwe7IfaZWMtyb2vdnQx1HJeAf8C78P/wNeDtwL3AP8EpicW3ci2VUHC0lXGKTylrTPX4Ez2HAn/WiyL6pFZFcobJfb58OpfBHwoVz5dmnbRWnfTXuIdxFZu+Jd6XFWxeM9iOyXyQvAMjp3Ilcu3o34HB1AdsXQX4ETS/7MXgy0A6vTe3sEWZvz9cD96e/43Palv8/dxPomsuaLe3Kf2QMqHO9rgDtTvPcCX0rllYy3Jva92ZA4KhmvhxwxM7NCBlXnuJmZDTwnDjMzK8SJw8zMCnHiMDOzQpw4zMysECcOMzMrxInDhiRJNyoN/y7pauWGre+DY58v6b19dbz+JOlwSWcMdBw2uA2qIUfMeiMiDhjoGMyGEtc4rDIkNSub3OrcNLjihZL2lfR/aYKZGZI2UzYB0m1pwpuZad+XSrpE2cRYl5INM9Fx3MWStkrLv5B0u7LJfY7MbfNMGl35bkl/lDSph3D/RdLNkh7oqH0oc2qKvVXSIal8746JedLzMyQdnpZPkXRfivu0VDZB0uXpNd4maa8u3q+XpNeWnwRskaRJkt6RJuu5Mw1e+aLXU1tzkvRMbvlz6dz3KE2CZNbBicOq5pXAt8mGjNgR+ADZcBefBU4gG2bhtxGxJ/Bm4FRJmwEfB/4REa8BTgb26OL4H46IPciGZTha0stT+WbAHyPitcBNwEd6iLMpxXUgcEoqezcwHXgtsG+KramrA0gaTzaUyy4p7q+mVd8Gvple43uAc+vtHxHryObFOCgd73XA4ohYBvwBeH1E7A5cQjZcd0MkvQ3YAZiRXs8ekv6l0f1t6HNTlVXNgxHRCiBpAXB9RISkVrIBAbchG0X0s2n70cC2ZLPpfQcgIu6RdE8Xxz9a0kFpeQrZF+TjwCqgo1ZwO9kkRd35Rfrivi/3a/5NwMURsZZsVNPfAXsCXQ0/vhJ4HjhX0q9y598X2FlaP9r1WEmbR8TTdY5xKdncMz8izeqWyrcBLk2JaxTZwHWNelt63JmejyF7n24qcAwbwpw4rGpeyC2vyz1fR/Z5XQu8JyIW5ndKX7I9zXmwN9mX8hsi4h9pqO3RafXq2DBw21p6/r+Rj1M1f2utoXPtfjRkk+lImgHsQ/al/wmy2d9ekmJ8rocYAG4BXilpAvAuNtRavgucHhHz0us+qbu4lL2BHfO4C/ifiCSWNuYAAAFfSURBVPhBA+e3YchNVTbYXAN8Mn3RIWn3VH4T2cyPKJuw5zV19h0HPJmSxo5kQ0/3pZuAQ5TNPDeBrBZ0K/AQWQ1iU0njyBJFx6RI4yLiauDTZM1CAL9hw0RkSJpOF1KyuwI4HWiLiMfTqnHAo2l5dhe7L2ZDk95Msrm5IXuPP5ziQ9JkSRNfvLsNV65x2GDzFbIpNu9JyWMxWT/D94EfpSaqu8i+sGv9GvhY2mYh8Mc+ju0Ksnmf7yar/Xw+Iv4GIGku2RDf97OhCWhz4EpJo8l+5X8mlR8NnJni3IQsIX2sm/NeSjYHw+G5spOAyyQ9SvY6p9XZ75x0/lvJhux+FiAifiNpJ+CWlJ+fAT7IhkmEbJjzsOpmZlaIm6rMzKwQN1WZdUHSicDBNcWXRcTJAxDLh4BP1RT/X0TM6e9YzNxUZWZmhbipyszMCnHiMDOzQpw4zMysECcOMzMr5P8DrJW2pG1cRfsAAAAASUVORK5CYII=\n",
+      "text/plain": [
+       "<Figure size 432x288 with 1 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "plt.figure(figsize= (6,4))\n",
+    "sns.histplot(testLabels - yPreds)\n",
+    "plt.title(\"Distribution of Residuals\")\n",
+    "plt.show()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 51,
+   "id": "1fc06194",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 360x360 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "data = pd.DataFrame({'TestLabels':testLabels , 'Predictions':yPreds}, columns=['TestLabels','Predictions'])\n",
+    "sns.set_theme(color_codes=True)\n",
+    "sns.lmplot(x='TestLabels', y='Predictions', data=data)\n",
+    "plt.show()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "a10af570",
+   "metadata": {},
+   "source": [
+    "## Evaluation Metrics for Regression model\n",
+    "\n",
+    "In the Previous cell we have visualized our model performance by plotting the best fit line. Now we will use various evaluation metrics to understand how well our model has performed.\n",
+    "\n",
+    "* Mean Absolute Error (MAE) is the sum of absolute differences between actual and predicted values, without considering the direction.\n",
+    "$$ MAE = \\frac{\\sum_{i=1}^n\\lvert y_{i} - \\hat{y_{i}}\\rvert} {n} $$\n",
+    "* Mean Squared Error (MSE) is calculated as the mean or average of the squared differences between predicted and expected target values in a dataset, a lower value is better.\n",
+    "$$ MSE = \\frac {1}{n} \\sum_{i=1}^n (y_{i} - \\hat{y_{i}})^2 $$\n",
+    "* Root Mean Squared Error (RMSE), Square root of MSE yields root mean square error (RMSE) it indicates the spread of the residual errors. It is always positive, and a lower value indicates better performance.\n",
+    "$$ RMSE = \\sqrt{\\frac {1}{n} \\sum_{i=1}^n (y_{i} - \\hat{y_{i}})^2} $$"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 52,
+   "id": "749f2a47",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "def MAE(y_true, y_pred):\n",
+    "    return np.mean(np.abs(y_pred-y_true))\n",
+    "\n",
+    "def MSE(y_true, y_pred):\n",
+    "    return np.mean(np.power(y_pred- y_true, 2))\n",
+    "\n",
+    "def RMSE(y_true, y_pred):\n",
+    "    return np.sqrt( np.mean(np.power(y_pred- y_true, 2)))"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 54,
+   "id": "161e7ee1",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "---- Evaluation Metrics ----\n",
+      "Mean Absoulte Error: 50639.27\n",
+      "Mean Squared Error: 4878809544.62\n",
+      "Root Mean Squared Error: 69848.48\n"
+     ]
+    }
+   ],
+   "source": [
+    "print(\"---- Evaluation Metrics ----\")\n",
+    "print(f\"Mean Absoulte Error: {MAE(testLabels, yPreds):.2f}\")\n",
+    "print(f\"Mean Squared Error: {MSE(testLabels, yPreds):.2f}\")\n",
+    "print(f\"Root Mean Squared Error: {RMSE(testLabels, yPreds):.2f}\")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "98bd24d3",
+   "metadata": {},
+   "source": [
+    "We can clearly see that the MAE is 49674, when compared with the median house value doesn't seems to be a good fit. \n",
+    "\n",
+    "Thus we can conclude that, the simple Linear Regression models is not being able to catch all the features.\n",
+    "So, maybe its time for you to try other algorithms. \n",
+    "<h5>NOTE : </h5> In the entire ML workflow, you never know exactly which model will perfrom the best. So, usually you try a lot of different algorithms to see which fits the model."
+   ]
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "Python 3",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.7.8"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 5
+}
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diff --git a/california_housing_price_prediction_with_linear_regression/california_housing_price_prediction_with_lr_cpp.ipynb b/california_housing_price_prediction_with_linear_regression/california_housing_price_prediction_with_lr_cpp.ipynb
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@@ -0,0 +1,813 @@
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "id": "048dbd39",
+   "metadata": {},
+   "source": [
+    "### Predicting  California House Prices with  Linear Regression\n",
+    "\n",
+    "### Objective\n",
+    "* To predict California Housing Prices using the most simple Linear Regression Model and see how it performs.\n",
+    "* To understand the modeling workflow using mlpack.\n",
+    "\n",
+    "### About the Data\n",
+    " This dataset is a modified version of the California Housing dataset available from Luís Torgo's page (University of Porto). Luís Torgo obtained it from the StatLib repository (which is closed now). The dataset may also be downloaded from StatLib mirrors.\n",
+    " \n",
+    " This dataset is also used in a book HandsOn-ML (a very good book and highly recommended)[ https://www.oreilly.com/library/view/hands-on-machine-learning/9781491962282/].\n",
+    " \n",
+    " The dataset in this directory is almost identical to the original, with two differences:\n",
+    "207 values were randomly removed from the totalbedrooms column, so we can discuss what to do with missing data. An additional categorical attribute called oceanproximity was added, indicating (very roughly) whether each block group is near the ocean, near the Bay area, inland or on an island. This allows discussing what to do with categorical data.\n",
+    "Note that the block groups are called \"districts\" in the Jupyter notebooks, simply because in some contexts the name \"block group\" was confusing.\"\n",
+    "\n",
+    "Lets look at the features of the dataset:\n",
+    "* Longitude : Longitude coordinate of the houses.\n",
+    "* Latitude : Latitude coordinate of the houses.\n",
+    "* Housing Median Age : Average lifespan of houses.\n",
+    "* Total Rooms : Number of rooms in a location.\n",
+    "* Total Bedrooms : Number of bedroooms in a location.\n",
+    "* Population : Population in that location.\n",
+    "* Median Income : Median Income of households in a location.\n",
+    "* Median House Value : Median House Value in a location.\n",
+    "* Ocean Proximity : Closeness to shore. \n",
+    "\n",
+    "### Approach\n",
+    " Here, we will try to recreate the workflow from the book mentioned above. \n",
+    " * Look at the Big Picture.\n",
+    " * Get the Data.\n",
+    " * Discover and Visualize the data to gain insights.\n",
+    " * Pre-Process the data for the Ml Algorithm.\n",
+    " * Create new features. \n",
+    " * Splitting the data.\n",
+    " * Training the ML model using MLPACK.\n",
+    " * Residuals, Errors and Conclusion.\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "1929f17d",
+   "metadata": {},
+   "source": [
+    "### Big Picture\n",
+    "\n",
+    "Suppose you work in a Real State Agency as an analyst or Data Scientist and your Boss wants you to predict the housing prices in a certain location. You are provided with a dataset. So, what will be the first thing to do?\n",
+    "\n",
+    "If you are probably jumping right into anaylsing the data and ML Algos, then this is a wrong a step. Its a big \"NO\". \n",
+    " <h5> The first thing is to ask Questions. </h5>\n",
+    " \n",
+    " Questions like :  What will be the predictions used for? Will it be fed into some other system or not? And Many More, just to have concrete goals.\n",
+    " \n",
+    " So, your boss says that they will be using the data to get the predcitions so that the other team can work on some investment strategies.\n",
+    " \n",
+    "So, let's get started."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "2a8513db",
+   "metadata": {},
+   "source": [
+    "<h3> Importing Header Files </h3>"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "id": "4d4ec4de",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "#include <mlpack/xeus-cling.hpp>\n",
+    "#include <mlpack/core.hpp>\n",
+    "#include <mlpack/methods/linear_regression/linear_regression.hpp>\n",
+    "#include <mlpack/core/data/split_data.hpp>"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "id": "8a0ace0c",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "#define WITHOUT_NUMPY 1\n",
+    "#include \"matplotlibcpp.h\"\n",
+    "#include \"xwidgets/ximage.hpp\"\n",
+    "\n",
+    "/* CPython Api Scripts for Plots */\n",
+    "\n",
+    "#include \"../utils/histogram.hpp\"\n",
+    "#include \"../utils/impute.hpp\"\n",
+    "#include \"../utils/pandasscatter.hpp\"\n",
+    "#include \"../utils/heatmap.hpp\"\n",
+    "#include \"../utils/plot.hpp\"\n",
+    "\n",
+    "namespace plt = matplotlibcpp;"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 3,
+   "id": "79e6d53d",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "using namespace mlpack;\n",
+    "using namespace mlpack::data;"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "2d5992b1",
+   "metadata": {},
+   "source": [
+    "<h3> Let's download the dataset. </h3>"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 4,
+   "id": "401c6664",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "!wget -q https://datasets.mlpack.org/examples/housing.csv"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "75b146bd",
+   "metadata": {},
+   "source": [
+    "### Loading the Data\n",
+    "Now, we need to load the dataset as armadillo matrix for further operations. Our dataset has a total of 9 features: 8 numerical and 1 categorical(ocean proximity). We need to map the categorical feature as armadillo operates on numeric values."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "08e417d5",
+   "metadata": {},
+   "source": [
+    "But, there's one thing which we need to do before loading the dataset as armadillo matrix, that is, to deal with any missing values. Since 207 values were removed from the original dataset from \"total_bedrooms_column\", we need to fill them using either \"mean\" or \"median\" of that feature( for numerical) and \"mode\"( for categorical\")."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 49,
+   "id": "7e4a6750",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "0"
+      ]
+     },
+     "execution_count": 49,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "// The imputing functions follows this:\n",
+    "// Impute(inputFile, outputFile, kind);\n",
+    "// Here, inputFile is our raw file, outputFile is our new file with the imputations, \n",
+    "// and kind refers to imputation method.\n",
+    "\n",
+    "Impute(\"housing.csv\", \"housing_imputed.csv\", \"median\");"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "ddba48dd",
+   "metadata": {},
+   "source": [
+    "Let's drop the headers using sed. Sed is a unix utility which is used to parse and transform text."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 50,
+   "id": "4d95bf63",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "!sed 1d housing_imputed.csv > housing_without_header.csv\n",
+    "\n",
+    "// Here, we used sed to delete the first row which is indicated by \"1d\" and created a new file with name\n",
+    "// housing_without_header.csv"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 51,
+   "id": "d2e2c3f4",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "arma::mat dataset;\n",
+    "data::DatasetInfo info;\n",
+    "info.Type(9) = mlpack::data::Datatype::categorical;\n",
+    "data::Load(\"housing_without_header.csv\", dataset, info);"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 52,
+   "id": "choice-victor",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "  -1.2223e+02  -1.2222e+02  -1.2224e+02  -1.2225e+02  -1.2225e+02  -1.2225e+02\n",
+      "   3.7880e+01   3.7860e+01   3.7850e+01   3.7850e+01   3.7850e+01   3.7850e+01\n",
+      "   4.1000e+01   2.1000e+01   5.2000e+01   5.2000e+01   5.2000e+01   5.2000e+01\n",
+      "   8.8000e+02   7.0990e+03   1.4670e+03   1.2740e+03   1.6270e+03   9.1900e+02\n",
+      "   1.2900e+02   1.1060e+03   1.9000e+02   2.3500e+02   2.8000e+02   2.1300e+02\n",
+      "   3.2200e+02   2.4010e+03   4.9600e+02   5.5800e+02   5.6500e+02   4.1300e+02\n",
+      "   1.2600e+02   1.1380e+03   1.7700e+02   2.1900e+02   2.5900e+02   1.9300e+02\n",
+      "   8.3252e+00   8.3014e+00   7.2574e+00   5.6431e+00   3.8462e+00   4.0368e+00\n",
+      "   4.5260e+05   3.5850e+05   3.5210e+05   3.4130e+05   3.4220e+05   2.6970e+05\n",
+      "            0            0            0            0            0            0\n",
+      "\n"
+     ]
+    }
+   ],
+   "source": [
+    "// Print the first 6 rows of the input data.\n",
+    "std::cout << dataset.submat(0, 0, dataset.n_rows - 1 , 5)<< std::endl;"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "a43f7359",
+   "metadata": {},
+   "source": [
+    "Did you notice something? Yes, the last row looks like it is entirely filled with '0'. Let's check our dataset to see what it corresponds to.\n",
+    "It corresponds to Ocean Proximity which is a categorical value, but here it is zero.\n",
+    "Why? It's because the load function loads numerical values only. This is exactly why we mapped Ocean proximity earlier.\n",
+    "So, let's deal with this."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 53,
+   "id": "d6969aa9",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "#include<mlpack/core/data/one_hot_encoding.hpp>\n",
+    "arma::mat encoded_dataset; \n",
+    "data::OneHotEncoding(dataset, encoded_dataset, info);"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "0f534207",
+   "metadata": {},
+   "source": [
+    "Here, we chose our pre-built encoding method \"One Hot Encoding\" to deal with the categorical values."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 54,
+   "id": "8bad850e",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "14"
+      ]
+     },
+     "execution_count": 54,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "encoded_dataset.n_rows\n",
+    "// The above code prints the number of rows(features + labels) in current dataset."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "89a8df9c",
+   "metadata": {},
+   "source": [
+    "You can notice the number of rows changed from 10 to 14, siginifying one hot encoding in this case."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "f078a9e5",
+   "metadata": {},
+   "source": [
+    "<h3>Visualization</h3>"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "b5ba850f",
+   "metadata": {},
+   "source": [
+    "Let's plot a histogram. "
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 8,
+   "id": "a7b59588",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "5c0dd57a133c4ecca91802380f610915",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "A Jupyter widget with unique id: 5c0dd57a133c4ecca91802380f610915"
+      ]
+     },
+     "execution_count": 8,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "// Hist(inputFile, bins, width, height, outputFile);\n",
+    "Hist(\"housing.csv\", 50, 20, 15, \"histogram.png\");\n",
+    "auto im = xw::image_from_file(\"histogram.png\").finalize();\n",
+    "im"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "ddcc2d3e",
+   "metadata": {},
+   "source": [
+    "Let's plot a scatter plot with longitude and latitude as x and y coordinates respectively."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 9,
+   "id": "54c2a0ca",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "f938371980f045b4b47b190bdc1dd973",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "A Jupyter widget with unique id: f938371980f045b4b47b190bdc1dd973"
+      ]
+     },
+     "execution_count": 9,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "// PandasScatter(inputFile, x, y, outputFile);\n",
+    "PandasScatter(\"housing.csv\", \"longitude\", \"latitude\", \"output.png\");\n",
+    "auto im = xw::image_from_file(\"output.png\").finalize();\n",
+    "im"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "5781bc1e",
+   "metadata": {},
+   "source": [
+    "Let's add some colour to the scatter plot."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "id": "3fef937e",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "8177cbf69b104cfeb24cbea0475693ae",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "A Jupyter widget with unique id: 8177cbf69b104cfeb24cbea0475693ae"
+      ]
+     },
+     "execution_count": 2,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "// PandasScatterColor(inputFile, x, y, label, c, outputFile);\n",
+    "PandasScatterColor(\"housing.csv\",\"longitude\",\"latitude\",\"Population\",\"median_house_value\",\"output1.png\");\n",
+    "auto im = xw::image_from_file(\"output1.png\").finalize();\n",
+    "im"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "431f719d",
+   "metadata": {},
+   "source": [
+    "Let's take it a step further and plot this on top of California map."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 11,
+   "id": "5d22bf50",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "10408985977f4b25b0332df8a43f7081",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "A Jupyter widget with unique id: 10408985977f4b25b0332df8a43f7081"
+      ]
+     },
+     "execution_count": 11,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "//PandasScatterMap(inputFile, imgFile, x, y, label, c, outputFile);\n",
+    "PandasScatterMap(\"housing.csv\",\"california.png\",\"longitude\",\"latitude\",\"Population\",\"median_house_value\",\"output2.png\");\n",
+    "auto im = xw::image_from_file(\"output2.png\").finalize();\n",
+    "im"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "36f8cbf3",
+   "metadata": {},
+   "source": [
+    "<h3>Correlation</h3>"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "id": "9c60a67f",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "98d1a64dbd0947d78f0f8e276debab93",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "A Jupyter widget with unique id: 98d1a64dbd0947d78f0f8e276debab93"
+      ]
+     },
+     "execution_count": 2,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "// HeatMap(inputFile, outputFile);\n",
+    "HeatMap(\"housing.csv\", \"heatmap.png\");\n",
+    "auto im = xw::image_from_file(\"heatmap.png\").finalize();\n",
+    "im"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "7d6af59e",
+   "metadata": {},
+   "source": [
+    "<h3>Train-Test Split</h3>\n",
+    "The dataset needs to be splitted into training and testing set for tarining."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 13,
+   "id": "chubby-water",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "// Labels are median_house_value which is row 8\n",
+    "arma::rowvec labels =\n",
+    "    arma::conv_to<arma::rowvec>::from(encoded_dataset.row(8));\n",
+    "encoded_dataset.shed_row(8);"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 14,
+   "id": "vital-lebanon",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "arma::mat trainSet, testSet;\n",
+    "arma::rowvec trainLabels, testLabels;"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 15,
+   "id": "ruled-refrigerator",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "// Split dataset randomly into training set and test set.\n",
+    "data::Split(encoded_dataset, labels, trainSet, testSet, trainLabels, testLabels,\n",
+    "    0.2 /* Percentage of dataset to use for test set. */);"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "57755813",
+   "metadata": {},
+   "source": [
+    "### Training the linear model\n",
+    "\n",
+    "Regression analysis is the most widely used method of prediction. Linear regression is used when the dataset has a linear correlation and as the name suggests, multiple linear regression has one independent variable (predictor) and one or more dependent variable(response).\n",
+    "\n",
+    "The simple linear regression equation is represented as y = $a + b_{1}x_{1} + b_{2}x_{2} + b_{3}x_{3} + ... + b_{n}x_{n}$ where $x_{i}$ is the ith explanatory variable, y is the dependent variable, $b_{i}$ is ith coefficient and a is the intercept.\n",
+    "\n",
+    "To perform linear regression we'll be using `LinearRegression()` api from mlpack."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 34,
+   "id": "chemical-inside",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "using namespace mlpack::regression;\n",
+    "LinearRegression lr(trainSet, trainLabels, 0.5);\n",
+    "// The above line creates and train the model."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 35,
+   "id": "sensitive-sociology",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "// Let's create a output vector for storing the results.\n",
+    "arma::rowvec output; \n",
+    "lr.Predict(testSet, output);"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 36,
+   "id": "empty-senator",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "lr.ComputeError(trainSet, trainLabels);"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 37,
+   "id": "circular-donna",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "4.74874e+09"
+     ]
+    }
+   ],
+   "source": [
+    "std::cout<<lr.ComputeError(trainSet, trainLabels);"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "d80c63d0",
+   "metadata": {},
+   "source": [
+    "Let's manually check some predictions."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 38,
+   "id": "laden-drawing",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "174300\n",
+      "190507\n"
+     ]
+    }
+   ],
+   "source": [
+    "std::cout << testLabels[1] << std::endl;\n",
+    "std::cout << output[1] << std::endl;"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 39,
+   "id": "72d31768",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "170500\n",
+      "203024\n"
+     ]
+    }
+   ],
+   "source": [
+    "std::cout << testLabels[7] << std::endl;\n",
+    "std::cout << output[7] << std::endl;"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 40,
+   "id": "19111678",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "arma::mat preds;\n",
+    "preds.insert_rows(0, testLabels);\n",
+    "preds.insert_rows(1, output);"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 41,
+   "id": "dc8e7e0e",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "mlpack::data::Save(\"preds.csv\", preds);"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "9eb7974b",
+   "metadata": {},
+   "source": [
+    "### Model Evaluation\n",
+    "Test data is visualized with `testLables` and `output`, the blue points indicates the data points and the blue line indicates the regression line or best fit line."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 42,
+   "id": "efc02267",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "2e350347bea84528bfc8ad34775eff1b",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "A Jupyter widget with unique id: 2e350347bea84528bfc8ad34775eff1b"
+      ]
+     },
+     "execution_count": 42,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "lmplot(\"predis.csv\", \"predsScatter\");\n",
+    "auto img = xw::image_from_file(\"predsScatter.png\").finalize();    \n",
+    "img"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 43,
+   "id": "95159c44",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "739a2b0617ff402ab08664e833fb9339",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "A Jupyter widget with unique id: 739a2b0617ff402ab08664e833fb9339"
+      ]
+     },
+     "execution_count": 43,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "histplot(\"predictions.csv\", \"Distribution of residuals\");\n",
+    "auto img = xw::image_from_file(\"Distribution of residuals.png\").finalize();\n",
+    "img"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "c677831f",
+   "metadata": {},
+   "source": [
+    "## Evaluation Metrics for Regression model\n",
+    "\n",
+    "In the Previous cell we have visualized our model performance by plotting the best fit line. Now we will use various evaluation metrics to understand how well our model has performed.\n",
+    "\n",
+    "* Mean Absolute Error (MAE) is the sum of absolute differences between actual and predicted values, without considering the direction.\n",
+    "$$ MAE = \\frac{\\sum_{i=1}^n\\lvert y_{i} - \\hat{y_{i}}\\rvert} {n} $$\n",
+    "* Mean Squared Error (MSE) is calculated as the mean or average of the squared differences between predicted and expected target values in a dataset, a lower value is better\n",
+    "$$ MSE = \\frac {1}{n} \\sum_{i=1}^n (y_{i} - \\hat{y_{i}})^2 $$\n",
+    "* Root Mean Squared Error (RMSE), Square root of MSE yields root mean square error (RMSE) it indicates the spread of the residual errors. It is always positive, and a lower value indicates better performance.\n",
+    "$$ RMSE = \\sqrt{\\frac {1}{n} \\sum_{i=1}^n (y_{i} - \\hat{y_{i}})^2} $$"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 44,
+   "id": "b9b5cf2e",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Mean Absolute Error: 49434.7\n",
+      "Mean Squared Error: 4.78e+09\n",
+      "Root Mean Squared Error: 69137.5\n"
+     ]
+    }
+   ],
+   "source": [
+    "// Model evaluation metrics.\n",
+    "\n",
+    "std::cout << \"Mean Absolute Error: \" << arma::mean(arma::abs(output - testLabels)) << std::endl;\n",
+    "std::cout << \"Mean Squared Error: \" << arma::mean(arma::pow(output - testLabels,2)) << std::endl;\n",
+    "std::cout << \"Root Mean Squared Error: \" << sqrt(arma::mean(arma::pow(output - testLabels,2))) << std::endl;"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "5c23ff83",
+   "metadata": {},
+   "source": [
+    "We can clearly see that the MAE is 49674, when compared with the median house value doesn't seems to be a good fit. \n",
+    "\n",
+    "Thus we can conclude that, the simple Linear Regression models is not being able to catch all the features.\n",
+    "So, maybe its time for you to try other algorithms. \n",
+    "<h5>NOTE : </h5> In the entire ML workflow, you never know exactly which model will perfrom the best. So, usually you try a lot of different algorithms to see which fits the model."
+   ]
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "C++14",
+   "language": "C++14",
+   "name": "xcpp14"
+  },
+  "language_info": {
+   "codemirror_mode": "text/x-c++src",
+   "file_extension": ".cpp",
+   "mimetype": "text/x-c++src",
+   "name": "c++",
+   "version": "14"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 5
+}
diff --git a/student_admission_regression_with_logistic_regression/student-admission-logistic-regression-cpp.ipynb b/student_admission_regression_with_logistic_regression/student-admission-logistic-regression-cpp.ipynb
index 9dfd3187..6ee3ad4b 100644
--- a/student_admission_regression_with_logistic_regression/student-admission-logistic-regression-cpp.ipynb
+++ b/student_admission_regression_with_logistic_regression/student-admission-logistic-regression-cpp.ipynb
@@ -1,278 +1,392 @@
 {
-  "metadata":{
-    "language_info":{
-      "codemirror_mode":"text/x-c++src",
-      "file_extension":".cpp",
-      "mimetype":"text/x-c++src",
-      "name":"c++",
-      "version":"14"
-    },
-    "kernelspec":{
-      "name":"xcpp14",
-      "display_name":"C++14",
-      "language":"C++14"
-    }
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "[![Binder](https://mybinder.org/badge_logo.svg)](https://lab.mlpack.org/v2/gh/mlpack/examples/master?urlpath=lab%2Ftree%2Fstudent_admission_regression_with_logistic_regression%2Fstudent-admission-logistic-regression-cpp.ipynb)"
+   ]
   },
-  "nbformat_minor":4,
-  "nbformat":4,
-  "cells":[
-    {
-      "cell_type":"markdown",
-      "source":"[![Binder](https://mybinder.org/badge_logo.svg)](https://lab.mlpack.org/v2/gh/mlpack/examples/master?urlpath=lab%2Ftree%2Fstudent_admission_regression_with_logistic_regression%2Fstudent-admission-logistic-regression-cpp.ipynb)",
-      "metadata":{
-
-      }
-    },
-    {
-      "cell_type":"code",
-      "source":"/**\n * @file student-admission-logistic-regression-cpp.ipynb\n *\n * A simple example usage of Logistic Regression (LR)\n * applied to the Student Admission dataset.\n *\n * We will use a Logistic-Regression model to predict whether a student\n * gets admitted into a university (i.e, the output classes are Yes or No),\n * based on their results on past exams.\n *\n * Data from Andrew Ng's Stanford University Machine Learning Course (Coursera).\n */",
-      "metadata":{
-        "trusted":true
-      },
-      "execution_count":null,
-      "outputs":[
-
-      ]
-    },
-    {
-      "cell_type":"code",
-      "source":"!wget -q https://lab.mlpack.org/data/student-admission.txt",
-      "metadata":{
-        "trusted":true
-      },
-      "execution_count":1,
-      "outputs":[
-
-      ]
-    },
-    {
-      "cell_type":"code",
-      "source":"#include <mlpack/xeus-cling.hpp>\n\n#include <mlpack/core.hpp>\n#include <mlpack/methods/logistic_regression/logistic_regression.hpp>",
-      "metadata":{
-        "trusted":true
-      },
-      "execution_count":2,
-      "outputs":[
-
-      ]
-    },
-    {
-      "cell_type":"code",
-      "source":"// Header files to create and show the plot.\n#define WITHOUT_NUMPY 1\n#include \"matplotlibcpp.h\"\n#include \"xwidgets/ximage.hpp\"\n\nnamespace plt = matplotlibcpp;",
-      "metadata":{
-        "trusted":true
-      },
-      "execution_count":3,
-      "outputs":[
-
-      ]
-    },
-    {
-      "cell_type":"code",
-      "source":"using namespace mlpack;",
-      "metadata":{
-        "trusted":true
-      },
-      "execution_count":4,
-      "outputs":[
-
-      ]
-    },
-    {
-      "cell_type":"code",
-      "source":"using namespace mlpack::regression;",
-      "metadata":{
-        "trusted":true
-      },
-      "execution_count":5,
-      "outputs":[
-
-      ]
-    },
-    {
-      "cell_type":"code",
-      "source":"// Read the input data.\narma::mat input;\ndata::Load(\"student-admission.txt\", input);",
-      "metadata":{
-        "trusted":true
-      },
-      "execution_count":6,
-      "outputs":[
-
-      ]
-    },
-    {
-      "cell_type":"code",
-      "source":"// Print the first 10 rows of the input data.\nstd::cout << input.submat(0, 0, input.n_rows - 1 , 10).t() << std::endl;",
-      "metadata":{
-        "trusted":true
-      },
-      "execution_count":7,
-      "outputs":[
-        {
-          "name":"stdout",
-          "text":"   34.6237   78.0247         0\n   30.2867   43.8950         0\n   35.8474   72.9022         0\n   60.1826   86.3086    1.0000\n   79.0327   75.3444    1.0000\n   45.0833   56.3164         0\n   61.1067   96.5114    1.0000\n   75.0247   46.5540    1.0000\n   76.0988   87.4206    1.0000\n   84.4328   43.5334    1.0000\n   95.8616   38.2253         0\n\n",
-          "output_type":"stream"
-        }
-      ]
-    },
-    {
-      "cell_type":"markdown",
-      "source":"Historical data from previous students: each student has two exams scores associated and the final admission result (1.0=yes, 0.0=no).",
-      "metadata":{
-
-      }
-    },
-    {
-      "cell_type":"code",
-      "source":"// Plot the input data.\n\n// Get the indices for the labels  0.0 (not admitted).\narma::mat dataset0 = input.cols(arma::find(input.row(2) == 0));\n\n// Get the data to for the indices.\nstd::vector<double> x0 = arma::conv_to<std::vector<double>>::from(dataset0.row(0));\nstd::vector<double> y0 = arma::conv_to<std::vector<double>>::from(dataset0.row(1));\n\n// Get the indices for the label 1.0 (admitted).\narma::mat dataset1 = input.cols(arma::find(input.row(2) == 1.0));\n\n// Get the data to for the indices.\nstd::vector<double> x1 = arma::conv_to<std::vector<double>>::from(dataset1.row(0));\nstd::vector<double> y1 = arma::conv_to<std::vector<double>>::from(dataset1.row(1));\n\nplt::figure_size(800, 800);\n\n// Set the label for the legend.\nstd::map<std::string, std::string> m0;\nm0.insert(std::pair<std::string, std::string>(\"label\", \"not admitted\"));\nplt::scatter(x0, y0, 4, m0);\n\n// Set the label for the legend.\nstd::map<std::string, std::string> m1;\nm1.insert(std::pair<std::string, std::string>(\"label\", \"admitted\"));\nplt::scatter(x1, y1, 4, m1);\n\nplt::xlabel(\"Exam 1 Score\");\nplt::ylabel(\"Exam 2 Score\");\nplt::title(\"Student admission vs. past two exams\");\nplt::legend();\n\nplt::save(\"./plot.png\");\nauto im = xw::image_from_file(\"plot.png\").finalize();\nim",
-      "metadata":{
-        "trusted":true
-      },
-      "execution_count":8,
-      "outputs":[
-        {
-          "execution_count":8,
-          "output_type":"execute_result",
-          "data":{
-            "application/vnd.jupyter.widget-view+json":{
-              "model_id":"75e1b93113f44ca2ad0a709098eae2c1",
-              "version_major":2,
-              "version_minor":0
-            },
-            "text/plain":"A Jupyter widget"
-          },
-          "metadata":{
-
-          }
-        }
-      ]
-    },
-    {
-      "cell_type":"markdown",
-      "source":"If the score of the first or the second exam was too low, it might be not enough to be admitted. You need a good balance.",
-      "metadata":{
-
-      }
-    },
-    {
-      "cell_type":"markdown",
-      "source":"This is the logistic function to model our admission:\n$P(y=1) = \\frac{1}{1 + e^{-(\\beta_{0} + \\beta_{1} \\cdot x_{1} + ... + \\beta_{n} \\cdot x_{n}) }}$\n\nwhere y is the admission result (0 or 1) and x are the exams scores.\nSince in our example the admission decision is based on two exams (x1 and x2)\n(two exams) we can set n = 2. The next step is to find the correct beta\nparameters for the model by using our historical data as a training set.",
-      "metadata":{
-
-      }
-    },
-    {
-      "cell_type":"code",
-      "source":"// Split data into training data X (input) and y (labels) target variable.\n\n// Labels are the last row.\narma::Row<size_t> labels =\n    arma::conv_to<arma::Row<size_t>>::from(input.row(input.n_rows - 1));\ninput.shed_row(input.n_rows - 1);",
-      "metadata":{
-        "trusted":true
-      },
-      "execution_count":9,
-      "outputs":[
-
-      ]
-    },
-    {
-      "cell_type":"code",
-      "source":"// Create and train Logistic Regression model.\n//\n// For more information checkout https://mlpack.org/doc/mlpack-git/doxygen/classmlpack_1_1regression_1_1LogisticRegression.html\n// or uncomment the line below.\n// ?LogisticRegression<>\nLogisticRegression<> lr(input, labels, 0.0 /* no regularization */);",
-      "metadata":{
-        "trusted":true
-      },
-      "execution_count":10,
-      "outputs":[
-
-      ]
-    },
-    {
-      "cell_type":"code",
-      "source":"// Final beta parameters.\nlr.Parameters().print()",
-      "metadata":{
-        "trusted":true
-      },
-      "execution_count":11,
-      "outputs":[
-        {
-          "name":"stdout",
-          "text":"  -25.1613    0.2062    0.2015\n",
-          "output_type":"stream"
-        }
-      ]
-    },
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "/**\n",
+    " * @file student-admission-logistic-regression-cpp.ipynb\n",
+    " *\n",
+    " * A simple example usage of Logistic Regression (LR)\n",
+    " * applied to the Student Admission dataset.\n",
+    " *\n",
+    " * We will use a Logistic-Regression model to predict whether a student\n",
+    " * gets admitted into a university (i.e, the output classes are Yes or No),\n",
+    " * based on their results on past exams.\n",
+    " *\n",
+    " * Data from Andrew Ng's Stanford University Machine Learning Course (Coursera).\n",
+    " */"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "!wget -q https://lab.mlpack.org/data/student-admission.txt"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 3,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "#include <mlpack/xeus-cling.hpp>\n",
+    "\n",
+    "#include <mlpack/core.hpp>\n",
+    "#include <mlpack/methods/logistic_regression/logistic_regression.hpp>"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 4,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "// Header files to create and show the plot.\n",
+    "#define WITHOUT_NUMPY 1\n",
+    "#include \"matplotlibcpp.h\"\n",
+    "#include \"xwidgets/ximage.hpp\"\n",
+    "\n",
+    "namespace plt = matplotlibcpp;"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 5,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "using namespace mlpack;"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 6,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "using namespace mlpack::regression;"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 7,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "// Read the input data.\n",
+    "arma::mat input;\n",
+    "data::Load(\"student-admission.txt\", input);"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 8,
+   "metadata": {},
+   "outputs": [
     {
-      "cell_type":"code",
-      "source":"// We can use these beta parameters to plot the decision boundary on the training data.\n// We only need two points to plot a line, so we choose two endpoints:\n// the min and the max among the X training data.\nstd::vector<double> xPlot;\nxPlot.push_back(arma::min(input.row(0)) - 2);\nxPlot.push_back(arma::max(input.row(0)) + 2);\n\nstd::vector<double> yPlot;\nyPlot.push_back((-1.0 / lr.Parameters()(2)) * (lr.Parameters()(1) * xPlot[0] + lr.Parameters()(0)));\nyPlot.push_back((-1.0 / lr.Parameters()(2)) * (lr.Parameters()(1) * xPlot[1] + lr.Parameters()(0)));",
-      "metadata":{
-        "trusted":true
-      },
-      "execution_count":12,
-      "outputs":[
-
-      ]
-    },
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "   34.6237   78.0247         0\n",
+      "   30.2867   43.8950         0\n",
+      "   35.8474   72.9022         0\n",
+      "   60.1826   86.3086    1.0000\n",
+      "   79.0327   75.3444    1.0000\n",
+      "   45.0833   56.3164         0\n",
+      "   61.1067   96.5114    1.0000\n",
+      "   75.0247   46.5540    1.0000\n",
+      "   76.0988   87.4206    1.0000\n",
+      "   84.4328   43.5334    1.0000\n",
+      "   95.8616   38.2253         0\n",
+      "\n"
+     ]
+    }
+   ],
+   "source": [
+    "// Print the first 10 rows of the input data.\n",
+    "std::cout << input.submat(0, 0, input.n_rows - 1 , 10).t() << std::endl;"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Historical data from previous students: each student has two exams scores associated and the final admission result (1.0=yes, 0.0=no)."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 9,
+   "metadata": {},
+   "outputs": [
     {
-      "cell_type":"code",
-      "source":"// Plot the decision boundary.\n\n// Get the indices for the labels  0.0 (not admitted).\narma::mat dataset0 = input.cols(arma::find(labels == 0));\n\n// Get the data to for the indices.\nstd::vector<double> x0 = arma::conv_to<std::vector<double>>::from(dataset0.row(0));\nstd::vector<double> y0 = arma::conv_to<std::vector<double>>::from(dataset0.row(1));\n\n// Get the indices for the label 1.0 (admitted).\narma::mat dataset1 = input.cols(arma::find(labels == 1.0));\n\n// Get the data to for the indices.\nstd::vector<double> x1 = arma::conv_to<std::vector<double>>::from(dataset1.row(0));\nstd::vector<double> y1 = arma::conv_to<std::vector<double>>::from(dataset1.row(1));\n\nplt::figure_size(800, 800);\nplt::scatter(x0, y0, 4);\nplt::scatter(x1, y1, 4);\n\nplt::plot(xPlot, yPlot);\n\nplt::xlabel(\"Exam 1 Score\");\nplt::ylabel(\"Exam 2 Score\");\nplt::title(\"Student admission vs. past two exams\");\n\nplt::save(\"./decision boundary-plot.png\");\nauto im = xw::image_from_file(\"decision boundary-plot.png\").finalize();\nim",
-      "metadata":{
-        "trusted":true
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "54466081d1d04875824c960df742de13",
+       "version_major": 2,
+       "version_minor": 0
       },
-      "execution_count":13,
-      "outputs":[
-        {
-          "execution_count":13,
-          "output_type":"execute_result",
-          "data":{
-            "application/vnd.jupyter.widget-view+json":{
-              "model_id":"06d78d253ec546e780ea8b5d129f0e1f",
-              "version_major":2,
-              "version_minor":0
-            },
-            "text/plain":"A Jupyter widget"
-          },
-          "metadata":{
-
-          }
-        }
+      "text/plain": [
+       "A Jupyter widget with unique id: 54466081d1d04875824c960df742de13"
       ]
-    },
-    {
-      "cell_type":"markdown",
-      "source":"The blue line is our decision boundary. When your exams score lie below the line then\nprobably (that is the prediction) you will not be admitted to University.\nIf they lie above, probably you will. As you can see, the boundary is not predicting\nperfectly on the training historical data.",
-      "metadata":{
-
-      }
-    },
+     },
+     "execution_count": 9,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "// Plot the input data.\n",
+    "\n",
+    "// Get the indices for the labels  0.0 (not admitted).\n",
+    "arma::mat dataset0 = input.cols(arma::find(input.row(2) == 0));\n",
+    "\n",
+    "// Get the data to for the indices.\n",
+    "std::vector<double> x0 = arma::conv_to<std::vector<double>>::from(dataset0.row(0));\n",
+    "std::vector<double> y0 = arma::conv_to<std::vector<double>>::from(dataset0.row(1));\n",
+    "\n",
+    "// Get the indices for the label 1.0 (admitted).\n",
+    "arma::mat dataset1 = input.cols(arma::find(input.row(2) == 1.0));\n",
+    "\n",
+    "// Get the data to for the indices.\n",
+    "std::vector<double> x1 = arma::conv_to<std::vector<double>>::from(dataset1.row(0));\n",
+    "std::vector<double> y1 = arma::conv_to<std::vector<double>>::from(dataset1.row(1));\n",
+    "\n",
+    "plt::figure_size(800, 800);\n",
+    "\n",
+    "// Set the label for the legend.\n",
+    "std::map<std::string, std::string> m0;\n",
+    "m0.insert(std::pair<std::string, std::string>(\"label\", \"not admitted\"));\n",
+    "plt::scatter(x0, y0, 4, m0);\n",
+    "\n",
+    "// Set the label for the legend.\n",
+    "std::map<std::string, std::string> m1;\n",
+    "m1.insert(std::pair<std::string, std::string>(\"label\", \"admitted\"));\n",
+    "plt::scatter(x1, y1, 4, m1);\n",
+    "\n",
+    "plt::xlabel(\"Exam 1 Score\");\n",
+    "plt::ylabel(\"Exam 2 Score\");\n",
+    "plt::title(\"Student admission vs. past two exams\");\n",
+    "plt::legend();\n",
+    "\n",
+    "plt::save(\"./plot.png\");\n",
+    "auto im = xw::image_from_file(\"plot.png\").finalize();\n",
+    "im"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "If the score of the first or the second exam was too low, it might be not enough to be admitted. You need a good balance."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "This is the logistic function to model our admission:\n",
+    "$P(y=1) = \\frac{1}{1 + e^{-(\\beta_{0} + \\beta_{1} \\cdot x_{1} + ... + \\beta_{n} \\cdot x_{n}) }}$\n",
+    "\n",
+    "where y is the admission result (0 or 1) and x are the exams scores.\n",
+    "Since in our example the admission decision is based on two exams (x1 and x2)\n",
+    "(two exams) we can set n = 2. The next step is to find the correct beta\n",
+    "parameters for the model by using our historical data as a training set."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 10,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "// Split data into training data X (input) and y (labels) target variable.\n",
+    "\n",
+    "// Labels are the last row.\n",
+    "arma::Row<size_t> labels =\n",
+    "    arma::conv_to<arma::Row<size_t>>::from(input.row(input.n_rows - 1));\n",
+    "input.shed_row(input.n_rows - 1);"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 11,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "// Create and train Logistic Regression model.\n",
+    "//\n",
+    "// For more information checkout https://mlpack.org/doc/mlpack-git/doxygen/classmlpack_1_1regression_1_1LogisticRegression.html\n",
+    "// or uncomment the line below.\n",
+    "// ?LogisticRegression<>\n",
+    "LogisticRegression<> lr(input, labels, 0.0 /* no regularization */);"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 12,
+   "metadata": {},
+   "outputs": [
     {
-      "cell_type":"code",
-      "source":"// Let's say that my scores are 40 in the first exam and 78 in the second one.\narma::mat scores(\"40.0; 78.0\");\n\narma::mat probabilities;\nlr.Classify(scores, probabilities);",
-      "metadata":{
-        "trusted":true
-      },
-      "execution_count":14,
-      "outputs":[
-
-      ]
-    },
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "  -25.1613    0.2062    0.2015\n"
+     ]
+    }
+   ],
+   "source": [
+    "// Final beta parameters.\n",
+    "lr.Parameters().print()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 13,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "// We can use these beta parameters to plot the decision boundary on the training data.\n",
+    "// We only need two points to plot a line, so we choose two endpoints:\n",
+    "// the min and the max among the X training data.\n",
+    "std::vector<double> xPlot;\n",
+    "xPlot.push_back(arma::min(input.row(0)) - 2);\n",
+    "xPlot.push_back(arma::max(input.row(0)) + 2);\n",
+    "\n",
+    "std::vector<double> yPlot;\n",
+    "yPlot.push_back((-1.0 / lr.Parameters()(2)) * (lr.Parameters()(1) * xPlot[0] + lr.Parameters()(0)));\n",
+    "yPlot.push_back((-1.0 / lr.Parameters()(2)) * (lr.Parameters()(1) * xPlot[1] + lr.Parameters()(0)));"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 14,
+   "metadata": {},
+   "outputs": [
     {
-      "cell_type":"code",
-      "source":"probabilities.print()",
-      "metadata":{
-        "trusted":true
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "e023c9b879234163b1f5498cc8154920",
+       "version_major": 2,
+       "version_minor": 0
       },
-      "execution_count":15,
-      "outputs":[
-        {
-          "name":"stdout",
-          "text":"   0.7680\n   0.2320\n",
-          "output_type":"stream"
-        }
+      "text/plain": [
+       "A Jupyter widget with unique id: e023c9b879234163b1f5498cc8154920"
       ]
-    },
+     },
+     "execution_count": 14,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "// Plot the decision boundary.\n",
+    "\n",
+    "// Get the indices for the labels  0.0 (not admitted).\n",
+    "arma::mat dataset0 = input.cols(arma::find(labels == 0));\n",
+    "\n",
+    "// Get the data to for the indices.\n",
+    "std::vector<double> x0 = arma::conv_to<std::vector<double>>::from(dataset0.row(0));\n",
+    "std::vector<double> y0 = arma::conv_to<std::vector<double>>::from(dataset0.row(1));\n",
+    "\n",
+    "// Get the indices for the label 1.0 (admitted).\n",
+    "arma::mat dataset1 = input.cols(arma::find(labels == 1.0));\n",
+    "\n",
+    "// Get the data to for the indices.\n",
+    "std::vector<double> x1 = arma::conv_to<std::vector<double>>::from(dataset1.row(0));\n",
+    "std::vector<double> y1 = arma::conv_to<std::vector<double>>::from(dataset1.row(1));\n",
+    "\n",
+    "plt::figure_size(800, 800);\n",
+    "plt::scatter(x0, y0, 4);\n",
+    "plt::scatter(x1, y1, 4);\n",
+    "\n",
+    "plt::plot(xPlot, yPlot);\n",
+    "\n",
+    "plt::xlabel(\"Exam 1 Score\");\n",
+    "plt::ylabel(\"Exam 2 Score\");\n",
+    "plt::title(\"Student admission vs. past two exams\");\n",
+    "\n",
+    "plt::save(\"./decision boundary-plot.png\");\n",
+    "auto im = xw::image_from_file(\"decision boundary-plot.png\").finalize();\n",
+    "im"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "The blue line is our decision boundary. When your exams score lie below the line then\n",
+    "probably (that is the prediction) you will not be admitted to University.\n",
+    "If they lie above, probably you will. As you can see, the boundary is not predicting\n",
+    "perfectly on the training historical data."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 15,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "// Let's say that my scores are 40 in the first exam and 78 in the second one.\n",
+    "arma::mat scores(\"40.0; 78.0\");\n",
+    "\n",
+    "arma::mat probabilities;\n",
+    "lr.Classify(scores, probabilities);"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 16,
+   "metadata": {},
+   "outputs": [
     {
-      "cell_type":"markdown",
-      "source":"Looks like my probability to be admitted at University is only 23%.",
-      "metadata":{
-
-      }
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "   0.7680\n",
+      "   0.2320\n"
+     ]
     }
-  ]
+   ],
+   "source": [
+    "probabilities.print()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Looks like my probability to be admitted at University is only 23%."
+   ]
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "C++14",
+   "language": "C++14",
+   "name": "xcpp14"
+  },
+  "language_info": {
+   "codemirror_mode": "text/x-c++src",
+   "file_extension": ".cpp",
+   "mimetype": "text/x-c++src",
+   "name": "c++",
+   "version": "14"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 4
 }
diff --git a/utils/heatmap.hpp b/utils/heatmap.hpp
new file mode 100644
index 00000000..e9a5e60b
--- /dev/null
+++ b/utils/heatmap.hpp
@@ -0,0 +1,105 @@
+// Inside the C++ notebook we can use:
+// HeatMap("filename.csv",width, height,"heatmap.png")
+
+#ifndef CHEATMAP_HPP
+#define CHEATMAP_HPP
+
+#define PY_SSIZE_T_CLEAN
+#include <Python.h>
+#include <string>
+
+// Here, we will use the same argument as used in python script heatmap.py
+// since this is what passed from the C++ notebook to python script.
+
+int HeatMap(const std::string& inFile,
+            const std::string& outFile = "histogram.png",
+            const int width = 15,
+            const int height = 10)
+{
+    // Calls python function cpandahist and plots the heatmap
+
+    PyObject *pName, *pModule, *pFunc;
+    PyObject *pArgs, *pValue;
+
+    // This has to be adapted if you run this on your local system,
+    // so whenever you call the python script it can find the correct
+    // module -> PYTHONPATH, on lab.mlpack.org we put all the utility
+    // functions in the utils folder so we add that path
+    // to the Python search path.
+    Py_Initialize();
+    PyRun_SimpleString("import sys");
+    PyRun_SimpleString("sys.path.append(\"../utils/\")");
+
+    // Name of python script without extension
+    pName = PyUnicode_DecodeFSDefault("heatmap");
+
+    pModule = PyImport_Import(pName);
+    Py_DECREF(pName);
+
+    if (pModule != NULL)
+    {
+        // The Python function from the histogram.py script
+        // we like to call - cheatmap
+        pFunc = PyObject_GetAttrString(pModule, "cheatmap");
+
+        if(pFunc && PyCallable_Check(pFunc))
+        {
+            // The number of arguments we pass to the python script.
+            // inFile, outFile, width, height
+            // for the function above it's 4
+            pArgs = PyTuple_New(4);
+
+            // Now we have to encode the argument to the correct type
+            // We can use PyLong_FromLong for width and height as they are integers
+            // As for rest, we can use PyString_FromString.
+
+            PyObject* pValueinFile = PyUnicode_FromString(inFile.c_str());
+            //Here we just set the index of the argument.
+            PyTuple_SetItem(pArgs, 0, pValueinFile);
+
+            PyObject* pValueoutFile = PyUnicode_FromString(outFile.c_str());
+            PyTuple_SetItem(pArgs, 1, pValueoutFile);
+
+            PyObject* pValuewidth = PyLong_FromLong(width);
+            PyTuple_SetItem(pArgs, 2, pValuewidth);
+
+            PyObject* pValueheight = PyLong_FromLong(height);
+            PyTuple_SetItem(pArgs, 3, pValueheight);
+
+            // The rest of the c++ part can remain same.
+            
+            pValue = PyObject_CallObject(pFunc, pArgs);
+            // We call the object with function name and arguments provided in c++ notebook.
+            Py_DECREF(pArgs);
+
+            if (pValue != NULL)
+            {
+                Py_DECREF(pValue);
+            }
+            else
+            {
+                Py_DECREF(pFunc);
+                Py_DECREF(pModule);
+                PyErr_Print();
+                fprintf(stderr,"Call failed.\n");
+                return 1;
+            }
+        }
+        else
+        {
+            if (PyErr_Occurred())
+              PyErr_Print();
+        }
+
+        Py_XDECREF(pFunc);
+        Py_DECREF(pModule);
+        }
+        else
+        {
+            PyErr_Print();
+            return -1;
+        }
+        return 0;
+    }
+
+#endif
diff --git a/utils/heatmap.py b/utils/heatmap.py
new file mode 100644
index 00000000..e0a87bdb
--- /dev/null
+++ b/utils/heatmap.py
@@ -0,0 +1,9 @@
+import pandas as pd
+import seaborn as sns
+import matplotlib.pyplot as plt
+
+def cheatmap(inFile, outFile='heatmap.png', width=15, height=10):
+    plt.figure(figsize=(width,height))
+    dataset = pd.read_csv(inFile)
+    sns.heatmap(dataset.corr(), annot=True)
+    plt.savefig(outFile)
\ No newline at end of file
diff --git a/utils/histogram.hpp b/utils/histogram.hpp
new file mode 100644
index 00000000..5a5fa33c
--- /dev/null
+++ b/utils/histogram.hpp
@@ -0,0 +1,110 @@
+// Inside the C++ notebook we can use:
+// Hist("filename.csv", "bins", "histogram.png")
+
+#ifndef CHISTOGRAM_HPP
+#define CHISTOGRAM_HPP
+
+#define PY_SSIZE_T_CLEAN
+#include <Python.h>
+#include <string>
+
+// Here, we will use the same argument as used in python script histogram.py
+// since this is what passed from the C++ notebook to python script
+
+int Hist(const std::string& inFile,
+         const int bins,
+         const int width = 20,
+         const int height = 15,
+         const std::string& outFile = "histogram.png")
+
+{
+    // Calls python function cpandahist and plots the histogram
+
+    PyObject *pName, *pModule, *pFunc;
+    PyObject *pArgs, *pValue;
+
+    // This has to be adapted if you run this on your local system,
+    // so whenever you call the python script it can find the correct
+    // module -> PYTHONPATH, on lab.mlpack.org we put all the utility
+    // functions in the utils folder so we add that path
+    // to the Python search path.
+    Py_Initialize();
+    PyRun_SimpleString("import sys");
+    PyRun_SimpleString("sys.path.append(\"../utils/\")");
+
+    // Name of python script without extension
+    pName = PyUnicode_DecodeFSDefault("histogram");
+
+    pModule = PyImport_Import(pName);
+    Py_DECREF(pName);
+
+    if (pModule != NULL)
+    {
+        // The Python function from the histogram.py script
+        // we like to call - cpandashist
+        pFunc = PyObject_GetAttrString(pModule, "cpandashist");
+
+        if (pFunc && PyCallable_Check(pFunc))
+        {
+            // The number of arguments we pass to the python script.
+            // inFile, outFile, kind
+            // for the function above it's 5
+            pArgs = PyTuple_New(5);
+
+            // Now we have to encode the argument to the correct type
+            // We can use PyLong_FromLong for bins, width and height as they are integers
+            // As for rest, we can use PyString_FromString
+
+            PyObject* pValueinFile = PyUnicode_FromString(inFile.c_str());
+            //Here we just set the index of the argument.
+            PyTuple_SetItem(pArgs, 0, pValueinFile);
+
+            PyObject* pValuebins = PyLong_FromLong(bins);
+            PyTuple_SetItem(pArgs, 1, pValuebins);
+
+            PyObject* pValuewidth = PyLong_FromLong(width);
+            PyTuple_SetItem(pArgs, 2, pValuewidth);
+
+            PyObject* pValueheight = PyLong_FromLong(height);
+            PyTuple_SetItem(pArgs, 3, pValueheight);
+
+            PyObject* pValueoutFile = PyUnicode_FromString(outFile.c_str());
+            PyTuple_SetItem(pArgs, 4, pValueoutFile);
+
+            // The rest of the c++ part can remain same.
+
+            pValue = PyObject_CallObject(pFunc, pArgs);
+            // We call the object with function name and arguments provided in c++ notebook.
+            Py_DECREF(pArgs);
+
+            if (pValue != NULL)
+            {
+                Py_DECREF(pValue);
+            }
+            else
+            {
+                Py_DECREF(pFunc);
+                Py_DECREF(pModule);
+                PyErr_Print();
+                fprintf(stderr,"Call failed.\n");
+                return 1;
+            }
+        }
+        else
+        {
+            if (PyErr_Occurred())
+              PyErr_Print();
+        }
+
+        Py_XDECREF(pFunc);
+        Py_DECREF(pModule);
+        }
+        else
+        {
+            PyErr_Print();
+            return -1;
+        }
+        return 0;
+    }
+
+#endif
diff --git a/utils/histogram.py b/utils/histogram.py
new file mode 100644
index 00000000..46d39500
--- /dev/null
+++ b/utils/histogram.py
@@ -0,0 +1,7 @@
+import pandas as pd
+import matplotlib.pyplot as plt
+
+def cpandashist(inFile, bins, width=20,height=15, outFile = 'histogram.png'):
+    dataset = pd.read_csv(inFile)
+    dataset.hist(bins = 50, figsize=(20,15))
+    plt.savefig(outFile)
diff --git a/utils/impute.hpp b/utils/impute.hpp
new file mode 100644
index 00000000..244a78fc
--- /dev/null
+++ b/utils/impute.hpp
@@ -0,0 +1,102 @@
+// Inside the C++ notebook we can use:
+// Impute("filename.csv", "output.csv", "imputationMethod")
+// imputationMethod can be "mean", "median", "method" depending upon missing values.
+
+#ifndef CIMPUTE_HPP
+#define CIMPUTE_HPP
+
+#define PY_SSIZE_T_CLEAN
+#include <Python.h>
+#include <string>
+
+// Here, we will use the same argument as used in python script impute.py
+// since this is what passed from the C++ notebook to python script.
+
+int Impute(const std::string& inFile,
+           const std::string& outFile,
+           const std::string& kind)
+{
+    // Calls python function Imputer and fills the missing values using
+    // the specified imputation policy and saves the dataset as .csv.
+    PyObject *pName, *pModule, *pFunc;
+    PyObject *pArgs, *pValue;
+
+    // This has to be adapted if you run this on your local system,
+    // so whenever you call the python script it can find the correct
+    // module -> PYTHONPATH, on lab.mlpack.org we put all the utility
+    // functions in the utils folder so we add that path
+    // to the Python search path.
+    Py_Initialize();
+    PyRun_SimpleString("import sys");
+    PyRun_SimpleString("sys.path.append(\"../utils/\")");
+
+    // Name of python script without extension.
+    pName = PyUnicode_DecodeFSDefault("impute");
+
+    pModule = PyImport_Import(pName);
+    Py_DECREF(pName);
+
+    if (pModule != NULL)
+    {
+        // The Python function from the impute.py script
+        // we like to call - cimputer
+        pFunc = PyObject_GetAttrString(pModule, "cimputer");
+
+        if(pFunc && PyCallable_Check(pFunc))
+        {
+            // The number of arguments we pass to the python script.
+            // inFile, outFile, kind
+            // for the function above it's 3
+            pArgs = PyTuple_New(3);
+
+            // Now we have to encode the argument to the correct type
+            // besides width , height everything else is a string.
+            // So we can use PyUnicode_FromString.
+
+            PyObject* pValueinFile = PyUnicode_FromString(inFile.c_str());
+            //Here we just set the index of the argument.
+            PyTuple_SetItem(pArgs, 0, pValueinFile);
+
+            PyObject* pValueoutFile = PyUnicode_FromString(outFile.c_str());
+            PyTuple_SetItem(pArgs, 1, pValueoutFile);
+
+            PyObject* pValuekind = PyUnicode_FromString(kind.c_str());
+            PyTuple_SetItem(pArgs, 2, pValuekind);
+
+            // The rest of the c++ part can remain same.
+
+            pValue = PyObject_CallObject(pFunc, pArgs);
+            // We call the object with function name and arguments provided in c++ notebook.
+            Py_DECREF(pArgs);
+
+            if (pValue != NULL)
+            {
+                Py_DECREF(pValue);
+            }
+            else
+            {
+                Py_DECREF(pFunc);
+                Py_DECREF(pModule);
+                PyErr_Print();
+                fprintf(stderr,"Call failed.\n");
+                return 1;
+            }
+        }
+        else
+        {
+            if (PyErr_Occurred())
+              PyErr_Print();
+        }
+
+        Py_XDECREF(pFunc);
+        Py_DECREF(pModule);
+        }
+        else
+        {
+            PyErr_Print();
+            return -1;
+        }
+        return 0;
+    }
+
+#endif
diff --git a/utils/impute.py b/utils/impute.py
new file mode 100644
index 00000000..5d4569a7
--- /dev/null
+++ b/utils/impute.py
@@ -0,0 +1,17 @@
+import pandas as pd
+import numpy as np
+
+def cimputer(inFile, outFile, kind):
+    dataset = pd.read_csv(inFile)
+    df = dataset.copy(deep=True)
+    for feature in df.columns:
+        if df[feature].dtype == "float":
+            if kind == "mean":
+                df[feature] = df[feature].fillna(df[feature].mean())
+            elif kind == "median":
+                df[feature] = df[feature].fillna(df[feature].median())
+            elif kind == "mode":
+                df[feature] = df[feature].fillna(df[feature].mode()[0])
+        elif df[feature].dtype == "object":
+            df[feature] = df[feature].fillna(df[feature].mode()[0])
+    df.to_csv(outFile, encoding='utf-8', index=False)
diff --git a/utils/pandasscatter.hpp b/utils/pandasscatter.hpp
new file mode 100644
index 00000000..ded0c721
--- /dev/null
+++ b/utils/pandasscatter.hpp
@@ -0,0 +1,280 @@
+// Inside the C++ notebook we can use:
+// PandasScatter("housing.csv", "longitude", "latitude", "output.png");
+// auto im = xw::image_from_file("output.png").finalize();
+// im
+
+#ifndef C_PANDAS_SCATTER_C_PANDAS_SCATTER_HPP
+#define C_PANDAS_SCATTER_C_PANDAS_SCATTER_HPP
+
+#define PY_SSIZE_T_CLEAN
+#include <Python.h>
+#include <string>
+
+// Here we use the same arguments as we used in the python script,
+// since this is what is passed from the C++ notebook to call the python script.
+int PandasScatter(const std::string& inFile,
+                  const std::string& x,
+                  const std::string& y,
+                  const std::string& outFile = "output.png",
+                  const int width = 10,
+                  const int height = 10)
+{
+  PyObject *pName, *pModule, *pFunc;
+  PyObject *pArgs, *pValue;
+  int i;
+
+  // This has to be adapted if you run this on your local system,
+  // so whenever you call the python script it can find the correct
+  // module -> PYTHONPATH, on lab.mlpack.org we put all the utility
+  // functions for plotting uinto the utils folder so we add that path
+  // to the Python search path.
+  Py_Initialize();
+  PyRun_SimpleString("import sys");
+  PyRun_SimpleString("sys.path.append(\"../utils/\")");
+  // Name of the python script without the extension.
+  pName = PyUnicode_DecodeFSDefault("pandasscatter");
+
+  pModule = PyImport_Import(pName);
+  Py_DECREF(pName);
+
+  if (pModule != NULL)
+  {
+    // The Python function from the pandasscatter.py script
+    // we like to call - cpandasscatter
+    pFunc = PyObject_GetAttrString(pModule, "cpandasscatter");
+
+    if (pFunc && PyCallable_Check(pFunc))
+    {
+      // The number of arguments we pass to the python script.
+      // inFile, x, y, outFile='output.png', height=10, width=10
+      // for the example above it's 6
+      pArgs = PyTuple_New(6);
+
+      // Now we have to encode the argument to the correct type
+      // besides width, height everything else is a string.
+      // So we can use PyUnicode_FromString.
+      // If the data is an int we can use PyLong_FromLong,
+      // see the lines below for an example.
+      PyObject* pValueinFile = PyUnicode_FromString(inFile.c_str());
+      // Here we just set the index of the argument.
+          PyTuple_SetItem(pArgs, 0, pValueinFile);
+
+      PyObject* pValueX = PyUnicode_FromString(x.c_str());
+      PyTuple_SetItem(pArgs, 1, pValueX);
+
+      PyObject* pValueY = PyUnicode_FromString(y.c_str());
+      PyTuple_SetItem(pArgs, 2, pValueY);
+
+      PyObject* pValueoutFile = PyUnicode_FromString(outFile.c_str());
+      PyTuple_SetItem(pArgs, 3, pValueoutFile);
+
+      PyObject* pValueWidth = PyLong_FromLong(width);
+      PyTuple_SetItem(pArgs, 4, pValueWidth);
+
+      PyObject* pValueHeight = PyLong_FromLong(height);
+      PyTuple_SetItem(pArgs, 5, pValueHeight);
+
+      // The rest of the c++ part can stay the same.
+
+      pValue = PyObject_CallObject(pFunc, pArgs);
+      Py_DECREF(pArgs);
+      if (pValue != NULL)
+      {
+        Py_DECREF(pValue);
+      }
+      else
+      {
+        Py_DECREF(pFunc);
+        Py_DECREF(pModule);
+        PyErr_Print();
+        fprintf(stderr,"Call failed.\n");
+        return 1;
+      }
+    }
+    else
+    {
+      if (PyErr_Occurred())
+        PyErr_Print();
+    }
+
+    Py_XDECREF(pFunc);
+    Py_DECREF(pModule);
+  }
+  else
+  {
+    PyErr_Print();
+    return 1;
+  }
+
+  return 0;
+}
+int PandasScatterColor(const std::string& inFile,
+                       const std::string& x,
+                       const std::string& y,
+                       const std::string& label,
+                       const std::string& c,
+                       const std::string& outFile,
+                       const int width = 10,
+                       const int height= 10)
+{
+  PyObject *pName, *pModule, *pFunc;
+  PyObject *pArgs, *pValue;
+  int i;
+  Py_Initialize();
+  PyRun_SimpleString("import sys");
+  PyRun_SimpleString("sys.path.append(\"../utils/\")");
+  pName = PyUnicode_DecodeFSDefault("pandasscatter");
+
+  pModule = PyImport_Import(pName);
+  Py_DECREF(pName);
+
+  if (pModule != NULL)
+  {
+    pFunc = PyObject_GetAttrString(pModule, "cpandasscattercolor");
+    if( pFunc && PyCallable_Check(pFunc))
+    {
+      pArgs = PyTuple_New(8);
+
+      PyObject* pValueinFile = PyUnicode_FromString(inFile.c_str());
+      PyTuple_SetItem(pArgs, 0, pValueinFile);
+
+      PyObject* pValueX = PyUnicode_FromString(x.c_str());
+      PyTuple_SetItem(pArgs, 1, pValueX);
+
+      PyObject* pValueY = PyUnicode_FromString(y.c_str());
+      PyTuple_SetItem(pArgs, 2, pValueY);
+
+      PyObject* pValueLabel = PyUnicode_FromString(label.c_str());
+      PyTuple_SetItem(pArgs, 3, pValueLabel);
+
+       PyObject* pValueC = PyUnicode_FromString(c.c_str());
+      PyTuple_SetItem(pArgs, 4, pValueC);
+
+      PyObject* pValueoutFile = PyUnicode_FromString(outFile.c_str());
+      PyTuple_SetItem(pArgs, 5, pValueoutFile);
+
+      PyObject* pValueWidth = PyLong_FromLong(width);
+      PyTuple_SetItem(pArgs, 6, pValueWidth);
+
+      PyObject* pValueHeight = PyLong_FromLong(height);
+      PyTuple_SetItem(pArgs, 7, pValueHeight);
+
+      pValue = PyObject_CallObject(pFunc, pArgs);
+      Py_DECREF(pArgs);
+      if (pValue != NULL)
+      {
+        Py_DECREF(pValue);
+      }
+      else
+      {
+        Py_DECREF(pFunc);
+        Py_DECREF(pModule);
+        PyErr_Print();
+        fprintf(stderr,"Call Failed.\n");
+        return 1;
+      }
+    }
+    else
+    {
+      if (PyErr_Occurred())
+        PyErr_Print();
+    }
+
+    Py_XDECREF(pFunc);
+    Py_DECREF(pModule);
+  }
+  else
+  {
+    PyErr_Print();
+    return -1;
+  }
+  return 0;
+}
+int PandasScatterMap(const std::string& inFile,
+                     const std::string& imgFile,
+                     const std::string& x,
+                     const std::string& y,
+                     const std::string& label,
+                     const std::string& c,
+                     const std::string& outFile,
+                     const int width = 10,
+                     const int height= 10)
+{
+  PyObject *pName, *pModule, *pFunc;
+  PyObject *pArgs, *pValue;
+  int i;
+  Py_Initialize();
+  PyRun_SimpleString("import sys");
+  PyRun_SimpleString("sys.path.append(\"../utils/\")");
+  pName = PyUnicode_DecodeFSDefault("pandasscatter");
+
+  pModule = PyImport_Import(pName);
+  Py_DECREF(pName);
+
+  if (pModule != NULL)
+  {
+    pFunc = PyObject_GetAttrString(pModule, "cpandasscattermap");
+    if(pFunc && PyCallable_Check(pFunc))
+    {
+      pArgs = PyTuple_New(9);
+
+      PyObject* pValueinFile = PyUnicode_FromString(inFile.c_str());
+      PyTuple_SetItem(pArgs, 0, pValueinFile);
+
+      PyObject* pValueimgFile = PyUnicode_FromString(imgFile.c_str());
+      PyTuple_SetItem(pArgs, 1, pValueimgFile);
+
+      PyObject* pValueX = PyUnicode_FromString(x.c_str());
+      PyTuple_SetItem(pArgs, 2, pValueX);
+
+      PyObject* pValueY = PyUnicode_FromString(y.c_str());
+      PyTuple_SetItem(pArgs, 3, pValueY);
+
+      PyObject* pValueLabel = PyUnicode_FromString(label.c_str());
+      PyTuple_SetItem(pArgs, 4, pValueLabel);
+
+      PyObject* pValueC = PyUnicode_FromString(c.c_str());
+      PyTuple_SetItem(pArgs, 5, pValueC);
+
+      PyObject* pValueoutFile = PyUnicode_FromString(outFile.c_str());
+      PyTuple_SetItem(pArgs, 6, pValueoutFile);
+
+      PyObject* pValueWidth = PyLong_FromLong(width);
+      PyTuple_SetItem(pArgs, 7, pValueWidth);
+
+      PyObject* pValueHeight = PyLong_FromLong(height);
+      PyTuple_SetItem(pArgs, 8, pValueHeight);
+
+      pValue = PyObject_CallObject(pFunc, pArgs);
+      Py_DECREF(pArgs);
+      if (pValue != NULL)
+      {
+        Py_DECREF(pValue);
+      }
+      else
+      {
+        Py_DECREF(pFunc);
+        Py_DECREF(pModule);
+        PyErr_Print();
+        fprintf(stderr,"Call Failed.\n");
+        return 1;
+      }
+
+    }
+    else
+    {
+      if (PyErr_Occurred())
+        PyErr_Print();
+    }
+
+    Py_XDECREF(pFunc);
+    Py_DECREF(pModule);
+  }
+  else
+  {
+    PyErr_Print();
+    return -1;
+  }
+  return 0;
+}
+#endif
diff --git a/utils/pandasscatter.py b/utils/pandasscatter.py
new file mode 100644
index 00000000..787178f3
--- /dev/null
+++ b/utils/pandasscatter.py
@@ -0,0 +1,28 @@
+from matplotlib import figure
+import pandas as pd
+import matplotlib.pyplot as plt
+from matplotlib.pyplot import figure
+
+def cpandasscatter(inFile, x, y, outFile='output.png', height=10, width=10):
+    dataset = pd.read_csv(inFile)
+    fig = dataset.plot(kind="scatter", x=x, y=y, alpha=0.1, figsize=(width, height))
+    fig.figure.savefig(outFile)
+
+def cpandasscattercolor(inFile, x, y, label, c, outFile='output1.png', height=10, width=10):
+    dataset = pd.read_csv(inFile)
+    fig = dataset.plot(kind="scatter", x=x, y=y, alpha=0.4,s=dataset["population"]/100,
+                       label=label, c=c, cmap=plt.get_cmap("jet"), colorbar=True,
+                       sharex = False)
+    fig.figure.savefig(outFile)
+
+def cpandasscattermap(inFile, imgFile, x, y, label, c, outFile="output2.png", height=10, width=7):
+    figure(figsize=(10,7))
+    im = plt.imread(imgFile)
+    dataset = pd.read_csv(inFile)
+    implot = plt.imshow(im, extent=[-124.55, -113.80, 32.45, 42.05], alpha=0.5,
+           cmap=plt.get_cmap("jet"))
+    plt.scatter(x=dataset[x], y=dataset[y], s=dataset["population"]/100, label=label, c=dataset[c], cmap=plt.get_cmap("jet"), alpha= 0.5)
+    plt.colorbar()
+    plt.ylabel("Latitude", fontsize=14)
+    plt.xlabel("Longitude", fontsize=14)
+    plt.savefig(outFile)
\ No newline at end of file
diff --git a/utils/plot.hpp b/utils/plot.hpp
index 6e4f6ef0..871fe6fb 100644
--- a/utils/plot.hpp
+++ b/utils/plot.hpp
@@ -18,14 +18,14 @@ int scatter(const std::string& fname,
             const int figWidth = 26,
             const int figHeight = 7)
 {
-  
+
   // Calls Python function cscatter and generates a scatter plot of Xcol and yCol and saves it,
   // so the plot can later be imported in C++ notebook using xwidget.
-    
+
   // PyObject contains info Python needs to treat a pointer to an object as an object.
   // It contains object's reference count and pointer to corresponding object type.
   PyObject *pName, *pModule, *pFunc, *pArgs, *pValue;
-    
+
   // Initialize Python Interpreter.
   Py_Initialize();
   // Import sys module in Interpreter and add current path to python search path.
@@ -43,43 +43,43 @@ int scatter(const std::string& fname,
   pArgs = PyTuple_New(12);
 
   // String object representing the name of the dataset to be loaded.
-  PyObject* pFname = PyString_FromString(fname.c_str());
+  PyObject* pFname = PyUnicode_FromString(fname.c_str());
   PyTuple_SetItem(pArgs, 0, pFname);
-    
+
   // String object representing the name of the feature to be plotted along X axis.
-  PyObject* pXcol = PyString_FromString(xCol.c_str());
+  PyObject* pXcol = PyUnicode_FromString(xCol.c_str());
   PyTuple_SetItem(pArgs, 1, pXcol);
-    
+
   // String object representing the name of the feature to be plotted along Y axis.
-  PyObject* pYcol = PyString_FromString(yCol.c_str());
+  PyObject* pYcol = PyUnicode_FromString(yCol.c_str());
   PyTuple_SetItem(pArgs, 2, pYcol);
-    
+
   // String object representing the name of the feature to be parsed as TimeStamp.
-  PyObject* pDateCol = PyString_FromString(dateCol.c_str());
+  PyObject* pDateCol = PyUnicode_FromString(dateCol.c_str());
   PyTuple_SetItem(pArgs, 3, pDateCol);
-  
+
   // String object representing the name of the feature to be used to mask the plot data points.
-  PyObject* pMaskCol = PyString_FromString(maskCol.c_str());
-  PyTuple_SetItem(pArgs, 4, pMaskCol);  
-  
+  PyObject* pMaskCol = PyUnicode_FromString(maskCol.c_str());
+  PyTuple_SetItem(pArgs, 4, pMaskCol);
+
   // String object representing the value for masking.
-  PyObject* pType = PyString_FromString(type.c_str());
+  PyObject* pType = PyUnicode_FromString(type.c_str());
   PyTuple_SetItem(pArgs, 5, pType);
-  
+
   // String object representing the feature name to be used as color value in plot.
-  PyObject* pColor = PyString_FromString(color.c_str());
+  PyObject* pColor = PyUnicode_FromString(color.c_str());
   PyTuple_SetItem(pArgs, 6, pColor);
-    
+
   // String object representing the X axis label.
-  PyObject* pXlabel = PyString_FromString(xLabel.c_str());
+  PyObject* pXlabel = PyUnicode_FromString(xLabel.c_str());
   PyTuple_SetItem(pArgs, 7, pXlabel);
-    
+
   // String object representing the Y axis label.
-  PyObject* pYlabel = PyString_FromString(yLabel.c_str());
+  PyObject* pYlabel = PyUnicode_FromString(yLabel.c_str());
   PyTuple_SetItem(pArgs, 8, pYlabel);
 
-  // String object representing the title of the figure. 
-  PyObject* pFigTitle = PyString_FromString(figTitle.c_str());
+  // String object representing the title of the figure.
+  PyObject* pFigTitle = PyUnicode_FromString(figTitle.c_str());
   PyTuple_SetItem(pArgs, 9, pFigTitle);
 
   // Integer object representing the width of the figure.
@@ -104,9 +104,9 @@ int barplot(const std::string& fname,
             const int figWidth = 5,
             const int figHeight = 7)
 {
-    
+
   // Calls Python function cbarplot and generates a barplot plot of x and y and saves it,
-  // so the plot can later be imported in C++ notebook using xwidget.  
+  // so the plot can later be imported in C++ notebook using xwidget.
 
   // PyObject contains info Python needs to treat a pointer to an object as an object.
   // It contains object's reference count and pointer to corresponding object type.
@@ -118,41 +118,41 @@ int barplot(const std::string& fname,
   PyRun_SimpleString("import sys");
   PyRun_SimpleString("sys.path.append(\"../utils/\")");
 
-  // Import the Python module.  
+  // Import the Python module.
   pName = PyUnicode_DecodeFSDefault("plot");
   pModule = PyImport_Import(pName);
 
-  // Get the reference to Python Function to call.  
+  // Get the reference to Python Function to call.
   pFunc = PyObject_GetAttrString(pModule, "cbarplot");
 
-  // Create a tuple object to hold the arguments for function call.  
+  // Create a tuple object to hold the arguments for function call.
   pArgs = PyTuple_New(7);
 
-  // String object representing the name of the dataset to be loaded.  
-  PyObject* pFname = PyString_FromString(fname.c_str());
+  // String object representing the name of the dataset to be loaded.
+  PyObject* pFname = PyUnicode_FromString(fname.c_str());
   PyTuple_SetItem(pArgs, 0, pFname);
 
   // String object representing the name of the feature to be plotted along X axis.
-  PyObject* pX = PyString_FromString(x.c_str());
+  PyObject* pX = PyUnicode_FromString(x.c_str());
   PyTuple_SetItem(pArgs, 1, pX);
 
   // String object representing the name of the feature to be plotted along Y axis.
-  PyObject* pY = PyString_FromString(y.c_str());
+  PyObject* pY = PyUnicode_FromString(y.c_str());
   PyTuple_SetItem(pArgs, 2, pY);
-    
+
   // String object representing the name of the feature to be parsed as TimeStamp.
-  PyObject* pDateCol = PyString_FromString(dateCol.c_str());
+  PyObject* pDateCol = PyUnicode_FromString(dateCol.c_str());
   PyTuple_SetItem(pArgs, 3, pDateCol);
 
-  // String object representing the title of the figure.  
-  PyObject* pFigTitle = PyString_FromString(figTitle.c_str());
+  // String object representing the title of the figure.
+  PyObject* pFigTitle = PyUnicode_FromString(figTitle.c_str());
   PyTuple_SetItem(pArgs, 4, pFigTitle);
 
-  // Integer object representing the width of the figure.  
+  // Integer object representing the width of the figure.
   PyObject* pFigWidth = PyLong_FromLong(figWidth);
   PyTuple_SetItem(pArgs, 5, pFigWidth);
 
-  // Integer object representing the height of the figure.  
+  // Integer object representing the height of the figure.
   PyObject* pFigHeight = PyLong_FromLong(figHeight);
   PyTuple_SetItem(pArgs, 6, pFigHeight);
 
@@ -171,50 +171,50 @@ int heatmap(const std::string& fname,
 {
 
   // PyObject contains info Python needs to treat a pointer to an object as an object.
-  // It contains object's reference count and pointer to corresponding object type.  
+  // It contains object's reference count and pointer to corresponding object type.
   PyObject *pName, *pModule, *pFunc, *pArgs, *pValue;
 
-  // Initialize Python Interpreter.  
+  // Initialize Python Interpreter.
   Py_Initialize();
-  // Import sys module in Interpreter and add current path to python search path.  
+  // Import sys module in Interpreter and add current path to python search path.
   PyRun_SimpleString("import sys");
   PyRun_SimpleString("sys.path.append(\"../utils/\")");
 
-  // Import the Python module.  
+  // Import the Python module.
   pName = PyUnicode_DecodeFSDefault("plot");
   pModule = PyImport_Import(pName);
 
-  // Get the reference to Python Function to call.  
+  // Get the reference to Python Function to call.
   pFunc = PyObject_GetAttrString(pModule, "cheatmap");
 
-  // Create a tuple object to hold the arguments for function call.  
+  // Create a tuple object to hold the arguments for function call.
   pArgs = PyTuple_New(6);
 
-  // String object representing the name of the dataset to be loaded.  
-  PyObject* pFname = PyString_FromString(fname.c_str());
+  // String object representing the name of the dataset to be loaded.
+  PyObject* pFname = PyUnicode_FromString(fname.c_str());
   PyTuple_SetItem(pArgs, 0, pFname);
 
   // String object representing the name of color map to be used for plotting.
-  PyObject* pColorMap = PyString_FromString(colorMap.c_str());
+  PyObject* pColorMap = PyUnicode_FromString(colorMap.c_str());
   PyTuple_SetItem(pArgs, 1, pColorMap);
 
-  // Boolean object indicating if correlation values must be annotated in figure.  
+  // Boolean object indicating if correlation values must be annotated in figure.
   PyObject* pAnnotation = PyBool_FromLong(annotation);
   PyTuple_SetItem(pArgs, 2, pAnnotation);
 
-  // String object representing the title of the figure.  
-  PyObject* pFigTitle = PyString_FromString(figTitle.c_str());
+  // String object representing the title of the figure.
+  PyObject* pFigTitle = PyUnicode_FromString(figTitle.c_str());
   PyTuple_SetItem(pArgs, 3, pFigTitle);
 
-  // Integer object representing the width of the figure.  
+  // Integer object representing the width of the figure.
   PyObject* pFigWidth = PyLong_FromLong(figWidth);
   PyTuple_SetItem(pArgs, 4, pFigWidth);
 
-  // Integer object representing the height of the figure.  
+  // Integer object representing the height of the figure.
   PyObject* pFigHeight = PyLong_FromLong(figHeight);
   PyTuple_SetItem(pArgs, 5, pFigHeight);
 
-  // Call the function by passing the reference to function & tuple holding arguments.  
+  // Call the function by passing the reference to function & tuple holding arguments.
   pValue = PyObject_CallObject(pFunc, pArgs);
 
   return 0;
@@ -227,42 +227,42 @@ int lmplot(const std::string& fname,
 {
 
   // PyObject contains info Python needs to treat a pointer to an object as an object.
-  // It contains object's reference count and pointer to corresponding object type.  
+  // It contains object's reference count and pointer to corresponding object type.
   PyObject *pName, *pModule, *pFunc, *pArgs, *pValue;
 
-  // Initialize Python Interpreter.  
+  // Initialize Python Interpreter.
   Py_Initialize();
   // Import sys module in Interpreter and add current path to python search path.
   PyRun_SimpleString("import sys");
   PyRun_SimpleString("sys.path.append(\"../utils/\")");
 
-  // Import the Python module.  
+  // Import the Python module.
   pName = PyUnicode_DecodeFSDefault("plot");
   pModule = PyImport_Import(pName);
 
-  // Get the reference to Python Function to call.  
+  // Get the reference to Python Function to call.
   pFunc = PyObject_GetAttrString(pModule, "clmplot");
 
-  // Create a tuple object to hold the arguments for function call.  
+  // Create a tuple object to hold the arguments for function call.
   pArgs = PyTuple_New(4);
 
-  // String object representing the name of the dataset to be loaded.  
-  PyObject* pFname = PyString_FromString(fname.c_str());
+  // String object representing the name of the dataset to be loaded.
+  PyObject* pFname = PyUnicode_FromString(fname.c_str());
   PyTuple_SetItem(pArgs, 0, pFname);
 
-  // String object representing the title of the figure.  
-  PyObject* pFigTitle = PyString_FromString(figTitle.c_str());
+  // String object representing the title of the figure.
+  PyObject* pFigTitle = PyUnicode_FromString(figTitle.c_str());
   PyTuple_SetItem(pArgs, 1, pFigTitle);
 
-  // Integer object representing the width of the figure.  
+  // Integer object representing the width of the figure.
   PyObject* pFigWidth = PyLong_FromLong(figWidth);
   PyTuple_SetItem(pArgs, 2, pFigWidth);
 
-  // Integer object representing the height of the figure.  
+  // Integer object representing the height of the figure.
   PyObject* pFigHeight = PyLong_FromLong(figHeight);
   PyTuple_SetItem(pArgs, 3, pFigHeight);
 
-  // Call the function by passing the reference to function & tuple holding arguments.  
+  // Call the function by passing the reference to function & tuple holding arguments.
   pValue = PyObject_CallObject(pFunc, pArgs);
 
   return 0;
@@ -275,42 +275,42 @@ int histplot(const std::string& fname,
 {
 
   // PyObject contains info Python needs to treat a pointer to an object as an object.
-  // It contains object's reference count and pointer to corresponding object type.  
+  // It contains object's reference count and pointer to corresponding object type.
   PyObject *pName, *pModule, *pFunc, *pArgs, *pValue;
 
-  // Initialize Python Interpreter.  
+  // Initialize Python Interpreter.
   Py_Initialize();
   // Import sys module in Interpreter and add current path to python search path.
   PyRun_SimpleString("import sys");
   PyRun_SimpleString("sys.path.append(\"../utils/\")");
 
-  // Import the Python module.  
+  // Import the Python module.
   pName = PyUnicode_DecodeFSDefault("plot");
   pModule = PyImport_Import(pName);
 
-  // Get the reference to Python Function to call.  
+  // Get the reference to Python Function to call.
   pFunc = PyObject_GetAttrString(pModule, "chistplot");
 
-  // Create a tuple object to hold the arguments for function call.  
+  // Create a tuple object to hold the arguments for function call.
   pArgs = PyTuple_New(4);
 
-  // String object representing the name of the dataset to be loaded.  
-  PyObject* pFname = PyString_FromString(fname.c_str());
+  // String object representing the name of the dataset to be loaded.
+  PyObject* pFname = PyUnicode_FromString(fname.c_str());
   PyTuple_SetItem(pArgs, 0, pFname);
 
-  // String object representing the title of the figure.  
-  PyObject* pFigTitle = PyString_FromString(figTitle.c_str());
+  // String object representing the title of the figure.
+  PyObject* pFigTitle = PyUnicode_FromString(figTitle.c_str());
   PyTuple_SetItem(pArgs, 1, pFigTitle);
 
-  // Integer object representing the width of the figure.  
+  // Integer object representing the width of the figure.
   PyObject* pFigWidth = PyLong_FromLong(figWidth);
   PyTuple_SetItem(pArgs, 2, pFigWidth);
 
-  // Integer object representing the height of the figure.  
+  // Integer object representing the height of the figure.
   PyObject* pFigHeight = PyLong_FromLong(figHeight);
   PyTuple_SetItem(pArgs, 3, pFigHeight);
 
-  // Call the function by passing the reference to function & tuple holding arguments.  
+  // Call the function by passing the reference to function & tuple holding arguments.
   pValue = PyObject_CallObject(pFunc, pArgs);
 
   return 0;
diff --git a/utils/plot.py b/utils/plot.py
index adebb5c0..19bf87ca 100644
--- a/utils/plot.py
+++ b/utils/plot.py
@@ -2,21 +2,21 @@
 import matplotlib.pyplot as plt
 import seaborn as sns
 
-def cscatter(filename: str, 
-             xCol: str, 
+def cscatter(filename: str,
+             xCol: str,
              yCol: str,
-             dateCol:str = None, 
-             maskCol: str = None, 
-             type_: str = None, 
+             dateCol:str = None,
+             maskCol: str = None,
+             type_: str = None,
              color: str = None,
-             xLabel: str = None, 
-             yLabel: str = None, 
-             figTitle: str = None, 
-             figWidth: int = 26, 
+             xLabel: str = None,
+             yLabel: str = None,
+             figTitle: str = None,
+             figWidth: int = 26,
              figHeight: int = 7) -> None:
     """
     Creates a scatter plot of size figWidth & figHeight, named figTitle and saves it.
-    
+
         Parameters:
             filename (str): Name of the dataset to load.
             xCol (str): Name of the feature in dataset to plot against X axis.
@@ -31,7 +31,7 @@ def cscatter(filename: str,
             figTitle (str): Title for the figure to be save; defaults to None.
             figWidth (int): Width of the figure; defaults to 26.
             figHeight (int): Height of the figure; defaults to 7.
-            
+
         Returns:
              (None): Function does not return anything.
     """
@@ -58,16 +58,16 @@ def cscatter(filename: str,
     plt.savefig(f"{figTitle}.png")
     plt.close()
 
-def cbarplot(filename: str, 
-             x: str, 
-             y: str, 
-             dateCol: str = None, 
-             figTitle: str = None, 
-             figWidth: int = 5, 
+def cbarplot(filename: str,
+             x: str,
+             y: str,
+             dateCol: str = None,
+             figTitle: str = None,
+             figWidth: int = 5,
              figHeight: int = 7) -> None:
     """
     Creates a bar plot of size figWidth & figHeight, named figTitle between x & y.
-    
+
         Parameters:
                 filename (str): Name of the dataset to load.
                 x (str): Name of the feature in dataset to plot against X axis.
@@ -91,16 +91,16 @@ def cbarplot(filename: str,
     plt.title(figTitle)
     plt.savefig(f"{figTitle}.png")
     plt.close()
-    
-def cheatmap(filename: str, 
-             cmap: str, 
-             annotate: bool, 
-             figTitle: str, 
-             figWidth: int = 12, 
+
+def cheatmap(filename: str,
+             cmap: str,
+             annotate: bool,
+             figTitle: str,
+             figWidth: int = 12,
              figHeight: int = 6) -> None:
     """
     Creates a heatmap (correlation map) of the dataset and saves it.
-    
+
         Parameters:
                 filename (str): Name of the dataset to load.
                 cmap (str): Name of the color map to be used for plotting.
@@ -120,14 +120,14 @@ def cheatmap(filename: str,
     plt.title(figTitle)
     plt.savefig(f"{figTitle}.png")
     plt.close()
-    
-def clmplot(filename: str, 
-            figTitle: str = None, 
-            figWidth: int = 6, 
+
+def clmplot(filename: str,
+            figTitle: str = None,
+            figWidth: int = 6,
             figHeight: int = 7) -> None:
     """
     Generates a regression plot on the given dataset and saves it.
-    
+
         Parameters:
                 filename (str): Name of the dataset to load.
                 figTitle (str): Title for the figure to be save; defaults to None.
@@ -144,14 +144,14 @@ def clmplot(filename: str,
     ax = sns.lmplot(x="Y_Test", y="Y_Preds", data=df)
     plt.savefig(f"{figTitle}.png")
     plt.close()
-    
-def chistplot(filename: str, 
-              figTitle: str = None, 
-              figWidth: int = 6, 
+
+def chistplot(filename: str,
+              figTitle: str = None,
+              figWidth: int = 6,
               figHeight: int = 4) -> None:
     """
     Generated a histogram on the given dataset and saves it.
-    
+
         Parameters:
                 filename (str): Name of the dataset to load.
                 figTitle (str): Title for the figure to be save; defaults to None.
@@ -169,4 +169,4 @@ def chistplot(filename: str,
     plt.title(f"{figTitle}")
     plt.savefig(f"{figTitle}.png")
     plt.close()
-    
+