Hamoye_wk2_Quiz_Answers.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [],
   "source": [
    "#importing Libraries\n",
    "import pandas as pd\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sns\n",
    "from sklearn.metrics import mean_absolute_error,r2_score,mean_squared_error\n",
    "from sklearn.linear_model import LinearRegression\n",
    "from sklearn.linear_model import Ridge\n",
    "from sklearn.linear_model import Lasso"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {
    "collapsed": true,
    "jupyter": {
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   "outputs": [
    {
     "data": {
      "text/html": [
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       "\n",
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       "    .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>date</th>\n",
       "      <th>Appliances</th>\n",
       "      <th>lights</th>\n",
       "      <th>T1</th>\n",
       "      <th>RH_1</th>\n",
       "      <th>T2</th>\n",
       "      <th>RH_2</th>\n",
       "      <th>T3</th>\n",
       "      <th>RH_3</th>\n",
       "      <th>T4</th>\n",
       "      <th>...</th>\n",
       "      <th>T9</th>\n",
       "      <th>RH_9</th>\n",
       "      <th>T_out</th>\n",
       "      <th>Press_mm_hg</th>\n",
       "      <th>RH_out</th>\n",
       "      <th>Windspeed</th>\n",
       "      <th>Visibility</th>\n",
       "      <th>Tdewpoint</th>\n",
       "      <th>rv1</th>\n",
       "      <th>rv2</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <td>0</td>\n",
       "      <td>2016-01-11 17:00:00</td>\n",
       "      <td>60</td>\n",
       "      <td>30</td>\n",
       "      <td>19.89</td>\n",
       "      <td>47.596667</td>\n",
       "      <td>19.2</td>\n",
       "      <td>44.790000</td>\n",
       "      <td>19.79</td>\n",
       "      <td>44.730000</td>\n",
       "      <td>19.000000</td>\n",
       "      <td>...</td>\n",
       "      <td>17.033333</td>\n",
       "      <td>45.53</td>\n",
       "      <td>6.600000</td>\n",
       "      <td>733.5</td>\n",
       "      <td>92.0</td>\n",
       "      <td>7.000000</td>\n",
       "      <td>63.000000</td>\n",
       "      <td>5.3</td>\n",
       "      <td>13.275433</td>\n",
       "      <td>13.275433</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>1</td>\n",
       "      <td>2016-01-11 17:10:00</td>\n",
       "      <td>60</td>\n",
       "      <td>30</td>\n",
       "      <td>19.89</td>\n",
       "      <td>46.693333</td>\n",
       "      <td>19.2</td>\n",
       "      <td>44.722500</td>\n",
       "      <td>19.79</td>\n",
       "      <td>44.790000</td>\n",
       "      <td>19.000000</td>\n",
       "      <td>...</td>\n",
       "      <td>17.066667</td>\n",
       "      <td>45.56</td>\n",
       "      <td>6.483333</td>\n",
       "      <td>733.6</td>\n",
       "      <td>92.0</td>\n",
       "      <td>6.666667</td>\n",
       "      <td>59.166667</td>\n",
       "      <td>5.2</td>\n",
       "      <td>18.606195</td>\n",
       "      <td>18.606195</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>2</td>\n",
       "      <td>2016-01-11 17:20:00</td>\n",
       "      <td>50</td>\n",
       "      <td>30</td>\n",
       "      <td>19.89</td>\n",
       "      <td>46.300000</td>\n",
       "      <td>19.2</td>\n",
       "      <td>44.626667</td>\n",
       "      <td>19.79</td>\n",
       "      <td>44.933333</td>\n",
       "      <td>18.926667</td>\n",
       "      <td>...</td>\n",
       "      <td>17.000000</td>\n",
       "      <td>45.50</td>\n",
       "      <td>6.366667</td>\n",
       "      <td>733.7</td>\n",
       "      <td>92.0</td>\n",
       "      <td>6.333333</td>\n",
       "      <td>55.333333</td>\n",
       "      <td>5.1</td>\n",
       "      <td>28.642668</td>\n",
       "      <td>28.642668</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>3</td>\n",
       "      <td>2016-01-11 17:30:00</td>\n",
       "      <td>50</td>\n",
       "      <td>40</td>\n",
       "      <td>19.89</td>\n",
       "      <td>46.066667</td>\n",
       "      <td>19.2</td>\n",
       "      <td>44.590000</td>\n",
       "      <td>19.79</td>\n",
       "      <td>45.000000</td>\n",
       "      <td>18.890000</td>\n",
       "      <td>...</td>\n",
       "      <td>17.000000</td>\n",
       "      <td>45.40</td>\n",
       "      <td>6.250000</td>\n",
       "      <td>733.8</td>\n",
       "      <td>92.0</td>\n",
       "      <td>6.000000</td>\n",
       "      <td>51.500000</td>\n",
       "      <td>5.0</td>\n",
       "      <td>45.410389</td>\n",
       "      <td>45.410389</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>4</td>\n",
       "      <td>2016-01-11 17:40:00</td>\n",
       "      <td>60</td>\n",
       "      <td>40</td>\n",
       "      <td>19.89</td>\n",
       "      <td>46.333333</td>\n",
       "      <td>19.2</td>\n",
       "      <td>44.530000</td>\n",
       "      <td>19.79</td>\n",
       "      <td>45.000000</td>\n",
       "      <td>18.890000</td>\n",
       "      <td>...</td>\n",
       "      <td>17.000000</td>\n",
       "      <td>45.40</td>\n",
       "      <td>6.133333</td>\n",
       "      <td>733.9</td>\n",
       "      <td>92.0</td>\n",
       "      <td>5.666667</td>\n",
       "      <td>47.666667</td>\n",
       "      <td>4.9</td>\n",
       "      <td>10.084097</td>\n",
       "      <td>10.084097</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 29 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                  date  Appliances  lights     T1       RH_1    T2       RH_2  \\\n",
       "0  2016-01-11 17:00:00          60      30  19.89  47.596667  19.2  44.790000   \n",
       "1  2016-01-11 17:10:00          60      30  19.89  46.693333  19.2  44.722500   \n",
       "2  2016-01-11 17:20:00          50      30  19.89  46.300000  19.2  44.626667   \n",
       "3  2016-01-11 17:30:00          50      40  19.89  46.066667  19.2  44.590000   \n",
       "4  2016-01-11 17:40:00          60      40  19.89  46.333333  19.2  44.530000   \n",
       "\n",
       "      T3       RH_3         T4  ...         T9   RH_9     T_out  Press_mm_hg  \\\n",
       "0  19.79  44.730000  19.000000  ...  17.033333  45.53  6.600000        733.5   \n",
       "1  19.79  44.790000  19.000000  ...  17.066667  45.56  6.483333        733.6   \n",
       "2  19.79  44.933333  18.926667  ...  17.000000  45.50  6.366667        733.7   \n",
       "3  19.79  45.000000  18.890000  ...  17.000000  45.40  6.250000        733.8   \n",
       "4  19.79  45.000000  18.890000  ...  17.000000  45.40  6.133333        733.9   \n",
       "\n",
       "   RH_out  Windspeed  Visibility  Tdewpoint        rv1        rv2  \n",
       "0    92.0   7.000000   63.000000        5.3  13.275433  13.275433  \n",
       "1    92.0   6.666667   59.166667        5.2  18.606195  18.606195  \n",
       "2    92.0   6.333333   55.333333        5.1  28.642668  28.642668  \n",
       "3    92.0   6.000000   51.500000        5.0  45.410389  45.410389  \n",
       "4    92.0   5.666667   47.666667        4.9  10.084097  10.084097  \n",
       "\n",
       "[5 rows x 29 columns]"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df=pd.read_csv('energydata_complete.csv')\n",
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {
    "collapsed": true,
    "jupyter": {
     "outputs_hidden": true
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "date           0\n",
       "Appliances     0\n",
       "lights         0\n",
       "T1             0\n",
       "RH_1           0\n",
       "T2             0\n",
       "RH_2           0\n",
       "T3             0\n",
       "RH_3           0\n",
       "T4             0\n",
       "RH_4           0\n",
       "T5             0\n",
       "RH_5           0\n",
       "T6             0\n",
       "RH_6           0\n",
       "T7             0\n",
       "RH_7           0\n",
       "T8             0\n",
       "RH_8           0\n",
       "T9             0\n",
       "RH_9           0\n",
       "T_out          0\n",
       "Press_mm_hg    0\n",
       "RH_out         0\n",
       "Windspeed      0\n",
       "Visibility     0\n",
       "Tdewpoint      0\n",
       "rv1            0\n",
       "rv2            0\n",
       "dtype: int64"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#to check if there is any column with missing data\n",
    "df.isnull().sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [],
   "source": [
    "#droping data nd light columns\n",
    "df.drop(['lights','date'],axis=1, inplace= True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [],
   "source": [
    "#scaling the variables in the dataset to give them same range.\n",
    "from sklearn.preprocessing import MinMaxScaler\n",
    "scaler=MinMaxScaler()\n",
    "normalized_df=pd.DataFrame(scaler.fit_transform(df),columns=df.columns)\n",
    "features_df=normalized_df.drop(columns=['Appliances'])\n",
    "target=normalized_df['Appliances']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [],
   "source": [
    "#Question 12\n",
    "#running a simple linear regresssion betwee column T2 and T6\n",
    "x=np.asanyarray(normalized_df[['T2']])\n",
    "y=np.asanyarray(normalized_df[['T6']])\n",
    "from sklearn.model_selection import train_test_split\n",
    "x_train,x_test,y_train,y_test=train_test_split(x,y,random_state=42)\n",
    "lm=LinearRegression()\n",
    "lm.fit(x_train,y_train)\n",
    "lm.score(x_test,y_test)\n",
    "predic_lm=lm.predict(x_test)\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0.64\n"
     ]
    }
   ],
   "source": [
    "#Question 12\n",
    "# The R^2 score\n",
    "rsquared=(r2_score(y_test,predic_lm))\n",
    "print(round(rsquared,2))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0.08\n"
     ]
    }
   ],
   "source": [
    "#Question 13\n",
    "#calculating the mean absolute error\n",
    "mae=mean_absolute_error(y_test,predic_lm)\n",
    "print(round(mae,2))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "55.14\n"
     ]
    }
   ],
   "source": [
    "#Question 14\n",
    "rss=np.sum(np.square(y_test-predic_lm))\n",
    "print(round(rss,2))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0.106\n"
     ]
    }
   ],
   "source": [
    "#Question 15\n",
    "#rmse\n",
    "rmse=np.sqrt(mean_squared_error(y_test,predic_lm))\n",
    "print(round(rmse,3))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {},
   "outputs": [],
   "source": [
    "# A multivariate regression model. To answer question 17-20\n",
    "x_train,x_test,y_train,y_test=train_test_split(features_df,target,test_size=0.3,random_state=42)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {},
   "outputs": [],
   "source": [
    "lm2=LinearRegression() \n",
    "lm2.fit(x_train,y_train)\n",
    "lm2.score(x_test,y_test)\n",
    "predic_lm=lm2.predict(x_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Features                    rv2\n",
      "linear_model_weight    0.553547\n",
      "dtype: object\n",
      "Features               Press_mm_hg\n",
      "linear_model_weight      -0.456698\n",
      "dtype: object\n"
     ]
    }
   ],
   "source": [
    "#question 17\n",
    "def get_weights_df(model, feat, col_name):\n",
    "  #this function returns the weight of every feature\n",
    "    weights = pd.Series(model.coef_, feat.columns).sort_values()\n",
    "    weights_df = pd.DataFrame(weights).reset_index()\n",
    "    weights_df.columns = ['Features', col_name]\n",
    "    weights_df[col_name].round(3)\n",
    "    return weights_df\n",
    "\n",
    "linear_model_weights = get_weights_df(lm2, x_train, 'linear_model_weight')\n",
    "print(linear_model_weights.max())\n",
    "print(linear_model_weights.min())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0.22380224717576866\n"
     ]
    }
   ],
   "source": [
    "#ridge\n",
    "# Using a ridge model \n",
    "from sklearn.linear_model import Ridge\n",
    "ridge=Ridge(alpha=0.4)\n",
    "ridge.fit(x_train,y_train)\n",
    "ridge.score(x_test,y_test)\n",
    "predic_ridge=ridge.predict(x_test)\n",
    "rmse=np.sqrt(mean_absolute_error(y_test,predic_ridge))\n",
    "print(rmse)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "r2 score on  training set is 0.02499234354320623\n",
      "r2 score on  test set is 0.026800880567125818\n",
      "mean absolute_error on predicted_values is :  0.055256639821262235 andr2 score is  0.026800880567125818\n",
      "number of features used=  4\n"
     ]
    }
   ],
   "source": [
    "from sklearn.linear_model import Lasso\n",
    "lasso=Lasso(alpha=0.001,max_iter=1000 )\n",
    "lasso.fit(x_train,y_train)\n",
    "lasso.score(x_test,y_test)\n",
    "predic_lasso=lasso.predict(x_test)\n",
    "print('r2 score on  training set is',lasso.score(x_train,y_train))\n",
    "print('r2 score on  test set is',lasso.score(x_test,y_test))\n",
    "print('mean absolute_error on predicted_values is : ',mean_absolute_error(y_test,predic_lasso), 'and' 'r2 score is ',r2_score(y_test,predic_lasso))\n",
    "print('number of features used= ',np.sum(lasso.coef_!=0))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0.094\n"
     ]
    }
   ],
   "source": [
    "rmse=np.sqrt(mean_squared_error(y_test,predic_lasso))\n",
    "print(round(rmse,3))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
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