PyData_Stack_Lab_updated.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# The PyData Stack\n",
    "\n",
    "In this lab, you should analyze the famous [Boston Housing Dataset](http://scikit-learn.org/stable/modules/generated/sklearn.datasets.load_boston.html).  Here's what you should do:\n",
    "\n",
    "* Load the data into a DataFrame and determine the purpose of the dataset.\n",
    "* Determine some basic statistics about the features.  Is there any missing data?  How do you know?\n",
    "* Decide what all the features mean, and their type (hint: explore the stats of each, and the object returned by `load_boston` in the link above).\n",
    "* Graph histograms of a few appropriate features, and describe what you see.\n",
    "* Graph scatter plots of the numerical features against the median house price.  What do you take from these graphs?\n",
    "* Slice the dataset _horizontally_, that is, use boolean indexing to create a sub-DataFrame from your data.  You should find a way to slice it that does something nontrivial: describe why what you've done is interesting (take a look at some of our previous notebooks for examples).\n",
    "* Create another graph that justifies that the above is interesting.\n",
    "* Knowing that this datset is typically used in regression to predict the value of the house, slice the data _vertically_, that is, remove some columns that you believe are not helpful for this task.  Justify your reasoning (from the steps you've taken before this).\n",
    "* Throughout all of the above, discuss what you're seeing in markdown cells, and use markdown appropriately to create headers to break up the analysis.  It's okay if you're wrong or just confused/unsure about your observations--this lab is about experimentation and playfulness with data!"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Citations\n",
    "* Dr. Z used as reference for boolean indexing, video about boolean indexing: https://www.youtube.com/watch?v=s4HxlWWVFRU\n",
    "* Website https://www.pluralsight.com/guides/a-lap-around-the-pydata-stack used for background info about PyData Stack\n",
    "* Stack Overflow used for information about dropping columns https://stackoverflow.com/questions/13411544/delete-a-column-from-a-pandas-dataframe, shared by Dakota Chang"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "#Import statments\n",
    "import io\n",
    "import numpy as np\n",
    "import pandas as pd\n",
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sns\n",
    "from itertools import combinations\n",
    "from sklearn import datasets\n",
    "from sklearn.linear_model import LinearRegression"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      ".. _boston_dataset:\n",
      "\n",
      "Boston house prices dataset\n",
      "---------------------------\n",
      "\n",
      "**Data Set Characteristics:**  \n",
      "\n",
      "    :Number of Instances: 506 \n",
      "\n",
      "    :Number of Attributes: 13 numeric/categorical predictive. Median Value (attribute 14) is usually the target.\n",
      "\n",
      "    :Attribute Information (in order):\n",
      "        - CRIM     per capita crime rate by town\n",
      "        - ZN       proportion of residential land zoned for lots over 25,000 sq.ft.\n",
      "        - INDUS    proportion of non-retail business acres per town\n",
      "        - CHAS     Charles River dummy variable (= 1 if tract bounds river; 0 otherwise)\n",
      "        - NOX      nitric oxides concentration (parts per 10 million)\n",
      "        - RM       average number of rooms per dwelling\n",
      "        - AGE      proportion of owner-occupied units built prior to 1940\n",
      "        - DIS      weighted distances to five Boston employment centres\n",
      "        - RAD      index of accessibility to radial highways\n",
      "        - TAX      full-value property-tax rate per $10,000\n",
      "        - PTRATIO  pupil-teacher ratio by town\n",
      "        - B        1000(Bk - 0.63)^2 where Bk is the proportion of blacks by town\n",
      "        - LSTAT    % lower status of the population\n",
      "        - MEDV     Median value of owner-occupied homes in $1000's\n",
      "\n",
      "    :Missing Attribute Values: None\n",
      "\n",
      "    :Creator: Harrison, D. and Rubinfeld, D.L.\n",
      "\n",
      "This is a copy of UCI ML housing dataset.\n",
      "https://archive.ics.uci.edu/ml/machine-learning-databases/housing/\n",
      "\n",
      "\n",
      "This dataset was taken from the StatLib library which is maintained at Carnegie Mellon University.\n",
      "\n",
      "The Boston house-price data of Harrison, D. and Rubinfeld, D.L. 'Hedonic\n",
      "prices and the demand for clean air', J. Environ. Economics & Management,\n",
      "vol.5, 81-102, 1978.   Used in Belsley, Kuh & Welsch, 'Regression diagnostics\n",
      "...', Wiley, 1980.   N.B. Various transformations are used in the table on\n",
      "pages 244-261 of the latter.\n",
      "\n",
      "The Boston house-price data has been used in many machine learning papers that address regression\n",
      "problems.   \n",
      "     \n",
      ".. topic:: References\n",
      "\n",
      "   - Belsley, Kuh & Welsch, 'Regression diagnostics: Identifying Influential Data and Sources of Collinearity', Wiley, 1980. 244-261.\n",
      "   - Quinlan,R. (1993). Combining Instance-Based and Model-Based Learning. In Proceedings on the Tenth International Conference of Machine Learning, 236-243, University of Massachusetts, Amherst. Morgan Kaufmann.\n",
      "\n"
     ]
    }
   ],
   "source": [
    "#load the dataset, load that into a dataframe, determine the purpose of the dataset\n",
    "boston_housing = datasets.load_boston()\n",
    "\n",
    "X = boston_housing.data\n",
    "\n",
    "df = pd.DataFrame(X, columns=boston_housing['feature_names'])\n",
    "\n",
    "#Print a description of the data \n",
    "print(boston_housing['DESCR'])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "After looking at the description of the columns of this dataset and reading Dr. Z's shared article, there are many problematic aspects of this dataset. Specifically, the B, LTSAT, DIS and CHAS columns seem to be racially charged to say the least, and therefore I will be dropping them from the dataset"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Index(['CRIM', 'ZN', 'INDUS', 'CHAS', 'NOX', 'RM', 'AGE', 'DIS', 'RAD', 'TAX',\n",
      "       'PTRATIO', 'B', 'LSTAT'],\n",
      "      dtype='object')\n"
     ]
    }
   ],
   "source": [
    "#Get column names to be removed\n",
    "print(df.columns)\n",
    "\n",
    "#Dropping columns of compromised data (B, LSTAT, DIS, CHAS)\n",
    "df.drop('B', axis=1, inplace=True)\n",
    "df.drop('LSTAT', axis=1, inplace=True)\n",
    "df.drop('DIS', axis=1, inplace=True)\n",
    "df.drop('CHAS', axis=1, inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>CRIM</th>\n",
       "      <th>ZN</th>\n",
       "      <th>INDUS</th>\n",
       "      <th>NOX</th>\n",
       "      <th>RM</th>\n",
       "      <th>AGE</th>\n",
       "      <th>RAD</th>\n",
       "      <th>TAX</th>\n",
       "      <th>PTRATIO</th>\n",
       "      <th>PRICE</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0.00632</td>\n",
       "      <td>18.0</td>\n",
       "      <td>2.31</td>\n",
       "      <td>0.538</td>\n",
       "      <td>6.575</td>\n",
       "      <td>65.2</td>\n",
       "      <td>1.0</td>\n",
       "      <td>296.0</td>\n",
       "      <td>15.3</td>\n",
       "      <td>24.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>0.02731</td>\n",
       "      <td>0.0</td>\n",
       "      <td>7.07</td>\n",
       "      <td>0.469</td>\n",
       "      <td>6.421</td>\n",
       "      <td>78.9</td>\n",
       "      <td>2.0</td>\n",
       "      <td>242.0</td>\n",
       "      <td>17.8</td>\n",
       "      <td>21.6</td>\n",
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       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>0.02729</td>\n",
       "      <td>0.0</td>\n",
       "      <td>7.07</td>\n",
       "      <td>0.469</td>\n",
       "      <td>7.185</td>\n",
       "      <td>61.1</td>\n",
       "      <td>2.0</td>\n",
       "      <td>242.0</td>\n",
       "      <td>17.8</td>\n",
       "      <td>34.7</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>0.03237</td>\n",
       "      <td>0.0</td>\n",
       "      <td>2.18</td>\n",
       "      <td>0.458</td>\n",
       "      <td>6.998</td>\n",
       "      <td>45.8</td>\n",
       "      <td>3.0</td>\n",
       "      <td>222.0</td>\n",
       "      <td>18.7</td>\n",
       "      <td>33.4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>0.06905</td>\n",
       "      <td>0.0</td>\n",
       "      <td>2.18</td>\n",
       "      <td>0.458</td>\n",
       "      <td>7.147</td>\n",
       "      <td>54.2</td>\n",
       "      <td>3.0</td>\n",
       "      <td>222.0</td>\n",
       "      <td>18.7</td>\n",
       "      <td>36.2</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
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      ],
      "text/plain": [
       "      CRIM    ZN  INDUS    NOX     RM   AGE  RAD    TAX  PTRATIO  PRICE\n",
       "0  0.00632  18.0   2.31  0.538  6.575  65.2  1.0  296.0     15.3   24.0\n",
       "1  0.02731   0.0   7.07  0.469  6.421  78.9  2.0  242.0     17.8   21.6\n",
       "2  0.02729   0.0   7.07  0.469  7.185  61.1  2.0  242.0     17.8   34.7\n",
       "3  0.03237   0.0   2.18  0.458  6.998  45.8  3.0  222.0     18.7   33.4\n",
       "4  0.06905   0.0   2.18  0.458  7.147  54.2  3.0  222.0     18.7   36.2"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#rename target feature to \"PRICE\", which is more accurate to what it represents\n",
    "df['PRICE'] = boston_housing['target']\n",
    "\n",
    "#View the new-look dataset\n",
    "df.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "In the above code block, I check that all columns have been dropped correctly, and rename the target feature to a more accurate name: 'PRICE'. By dropping problematic columns, many of my ethical concerns have been resolved and I can move on to analyzing the data."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "count    506.000000\n",
       "mean      68.574901\n",
       "std       28.148861\n",
       "min        2.900000\n",
       "25%       45.025000\n",
       "50%       77.500000\n",
       "75%       94.075000\n",
       "max      100.000000\n",
       "Name: AGE, dtype: float64"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df['AGE'].describe()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "In the above code block, I view the description of the AGE feature of the dataset to get a good sense of how it should be graphed. Its distribution between minimum, 25%, 50%, 75% and max is relatively even and has a very manageable range (0-100), so I don't think I will have to do any manipluation to graph the AGE data in a clear fashion."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0, 0.5, 'Price')"
      ]
     },
     "execution_count": 6,
     "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": [
    "#plot data in a scatterplot\n",
    "plt.scatter(df['AGE'], df['PRICE'], alpha = 0.4)\n",
    "plt.title(\"Age vs Price\")\n",
    "plt.xlabel(\"Age\")\n",
    "plt.ylabel(\"Price\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "In the above graph, I compare the age of a house to its price. I figured that this comparison would be interesting becuase typically there is a direct correlaion between the price of a house and its age, as potential home owners are willing to pay far more new houses and very old houses (+200) than houses that have an age somewhere between that. However, this scatter plot shows that there is little to no coorelation between the two. This lack of correlation between the two is different from many other areas of the US, which means that there must be something special about this dataset. Perhaps there was error in its recording, or maybe there is a special factor about Boston, its history for example, that keeps the value of houses relatively constant."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "count    506.000000\n",
       "mean       6.284634\n",
       "std        0.702617\n",
       "min        3.561000\n",
       "25%        5.885500\n",
       "50%        6.208500\n",
       "75%        6.623500\n",
       "max        8.780000\n",
       "Name: RM, dtype: float64"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df['RM'].describe()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Similar to what I did with the AGE feature, here I am again checking for anything out of the ordinary in the RM dataset. There are no huge outliers, and all vlaues seem to be logical. However, the mean of this column, 6.2 rooms per dwelling, seems very high compared to the national average, which indicates that many high-income areas may be represented in the data in comparison to lower-income areas."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0.5, 1.0, 'Price by Number of Rooms')"
      ]
     },
     "execution_count": 8,
     "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": [
    "#plotting RM data in a histogram to determine how much the price changes based upon the amount of rooms.\n",
    "plt.hist(df[df[\"RM\"] > 6.2][\"PRICE\"], alpha=0.5, color='b')\n",
    "plt.hist(df[df[\"RM\"] < 6.2][\"PRICE\"], alpha=0.5, color='y')\n",
    "plt.title(\"Price by Number of Rooms\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0, 0.5, 'Price')"
      ]
     },
     "execution_count": 9,
     "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": [
    "plt.scatter(df['RM'], df['PRICE'], alpha = 0.4)\n",
    "plt.title(\"Amount of Rooms vs Price\")\n",
    "plt.xlabel(\"Rooms\")\n",
    "plt.ylabel(\"Price\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "In the two above graphs, I display the strong correlation between the number of rooms and price of the houses. This is very clear in the histogram, in which there is little overlap, showing that the price ranges for the two amounts of rooms (<6.2, >6.2) are very different. However, I noticed a small overlap in which there was an area in which the price was very high despite having an average number of rooms below 6.2. This tells me that there must be someting special about this area that could use further investigation. "
   ]
  }
 ],
 "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": "3.7.6"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 4
}