HAMOYE Data Science Internship Stage 2 Assessment.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 83,
   "id": "f3e83f24",
   "metadata": {},
   "outputs": [],
   "source": [
    "#importing libraries\n",
    "import pandas as pd\n",
    "import numpy as np\n",
    "\n",
    "from sklearn.preprocessing import MinMaxScaler\n",
    "from sklearn.preprocessing import StandardScaler\n",
    "from sklearn.model_selection import train_test_split\n",
    "from sklearn.linear_model import LinearRegression\n",
    "from sklearn.metrics import r2_score\n",
    "from sklearn.metrics import mean_squared_error\n",
    "from sklearn.metrics import mean_absolute_error\n",
    "from sklearn.linear_model import Ridge\n",
    "from sklearn.linear_model import Lasso"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "id": "9f6c2f20",
   "metadata": {},
   "outputs": [],
   "source": [
    "df=pd.read_csv(\"C:\\\\Users\\\\ACE\\\\Desktop\\\\energydata_complete.csv\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "id": "5a129a11",
   "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>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",
       "      <th>0</th>\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",
       "      <th>1</th>\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",
       "      <th>2</th>\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",
       "      <th>3</th>\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",
       "      <th>4</th>\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": 27,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "dc12e603",
   "metadata": {},
   "source": [
    "Number 12: \n",
    "From the dataset, fit a linear model on the relationship between the temperature in the living room\n",
    "in Celsius (x = T2) and the temperature outside the building (y = T6).\n",
    "What is the R^2 value in two d.p.?"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "id": "f2c6e835",
   "metadata": {},
   "outputs": [],
   "source": [
    "x=df.T2.values.reshape(-1,1)\n",
    "y=df.T6"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "id": "6b1ac6ed",
   "metadata": {},
   "outputs": [],
   "source": [
    "x_train,x_test,y_train,y_test=train_test_split(x,y,test_size=0.25,random_state=42)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "id": "18f75f92",
   "metadata": {},
   "outputs": [],
   "source": [
    "LR=LinearRegression().fit(x_train,y_train)\n",
    "y_pred=LR.predict(x_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "id": "f9ef273b",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Coefficient of determination (R^2): 0.64\n"
     ]
    }
   ],
   "source": [
    "print('Coefficient of determination (R^2): %.2f'\n",
    "      % r2_score(y_test, y_pred))"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "b63859ae",
   "metadata": {},
   "source": [
    "Number 13:\n",
    "Normalize the dataset using the MinMaxScaler after removing the following columns: [“date”, “lights”]. \n",
    "The target variable is “Appliances”. Use a 70-30 train-test set split with a random state of 42 (for reproducibility).\n",
    "Run a multiple linear regression using the training set and evaluate your model on the test set. \n",
    "Answer the following questions:\n",
    "\n",
    "What is the Mean Absolute Error (in two decimal places)?\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "id": "a768af4f",
   "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>Appliances</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>RH_4</th>\n",
       "      <th>T5</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",
       "      <th>0</th>\n",
       "      <td>60</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>45.566667</td>\n",
       "      <td>17.166667</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",
       "      <th>1</th>\n",
       "      <td>60</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>45.992500</td>\n",
       "      <td>17.166667</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",
       "      <th>2</th>\n",
       "      <td>50</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>45.890000</td>\n",
       "      <td>17.166667</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",
       "      <th>3</th>\n",
       "      <td>50</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>45.723333</td>\n",
       "      <td>17.166667</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",
       "      <th>4</th>\n",
       "      <td>60</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>45.530000</td>\n",
       "      <td>17.200000</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 × 27 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "   Appliances     T1       RH_1    T2       RH_2     T3       RH_3         T4  \\\n",
       "0          60  19.89  47.596667  19.2  44.790000  19.79  44.730000  19.000000   \n",
       "1          60  19.89  46.693333  19.2  44.722500  19.79  44.790000  19.000000   \n",
       "2          50  19.89  46.300000  19.2  44.626667  19.79  44.933333  18.926667   \n",
       "3          50  19.89  46.066667  19.2  44.590000  19.79  45.000000  18.890000   \n",
       "4          60  19.89  46.333333  19.2  44.530000  19.79  45.000000  18.890000   \n",
       "\n",
       "        RH_4         T5  ...         T9   RH_9     T_out  Press_mm_hg  RH_out  \\\n",
       "0  45.566667  17.166667  ...  17.033333  45.53  6.600000        733.5    92.0   \n",
       "1  45.992500  17.166667  ...  17.066667  45.56  6.483333        733.6    92.0   \n",
       "2  45.890000  17.166667  ...  17.000000  45.50  6.366667        733.7    92.0   \n",
       "3  45.723333  17.166667  ...  17.000000  45.40  6.250000        733.8    92.0   \n",
       "4  45.530000  17.200000  ...  17.000000  45.40  6.133333        733.9    92.0   \n",
       "\n",
       "   Windspeed  Visibility  Tdewpoint        rv1        rv2  \n",
       "0   7.000000   63.000000        5.3  13.275433  13.275433  \n",
       "1   6.666667   59.166667        5.2  18.606195  18.606195  \n",
       "2   6.333333   55.333333        5.1  28.642668  28.642668  \n",
       "3   6.000000   51.500000        5.0  45.410389  45.410389  \n",
       "4   5.666667   47.666667        4.9  10.084097  10.084097  \n",
       "\n",
       "[5 rows x 27 columns]"
      ]
     },
     "execution_count": 32,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "new_df = df.drop(['date', 'lights'], axis=1)\n",
    "new_df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "id": "abf8bd7b",
   "metadata": {},
   "outputs": [],
   "source": [
    "scaler = MinMaxScaler()\n",
    "normalized_df = pd.DataFrame(scaler.fit_transform(new_df), columns=new_df.columns)\n",
    "\n",
    "features = normalized_df.drop(columns='Appliances')\n",
    "target = normalized_df['Appliances']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 99,
   "id": "9c148a14",
   "metadata": {},
   "outputs": [],
   "source": [
    "x_tra,x_tes,y_tra,y_tes=train_test_split(features,target,test_size=0.3,random_state=42)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 100,
   "id": "5e0a64f8",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "LinearRegression()"
      ]
     },
     "execution_count": 100,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "linear= LinearRegression()\n",
    "linear.fit(x_tra, y_tra)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 101,
   "id": "8805187c",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "MAE: 8.16\n"
     ]
    }
   ],
   "source": [
    "pred = linear.predict(x_tes)\n",
    "MAE = mean_absolute_error(y_test,pred)\n",
    "print(f'MAE: {round(MAE,2)}')"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "c857201c",
   "metadata": {},
   "source": [
    "Number 14:\n",
    "What is the Residual Sum of Squares (in two decimal places)?"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 89,
   "id": "d881776a",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Residual Sum of Squares: 45.35\n"
     ]
    }
   ],
   "source": [
    "rss = np.sum(np.square(y_tes - pred))\n",
    "print(f'Residual Sum of Squares: {round(rss, 2)}')"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "2c3f7566",
   "metadata": {},
   "source": [
    "Number 15:\n",
    "What is the Root Mean Squared Error (in three decimal places)?"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 90,
   "id": "900ff0f7",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "RMSE: 0.088\n"
     ]
    }
   ],
   "source": [
    "rmse = np.sqrt(mean_squared_error(y_tes, pred))\n",
    "print(f'RMSE: {round(rmse, 3)}')"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "d99aa4d6",
   "metadata": {},
   "source": [
    "Number 16\n",
    "What is the Coefficient of Determination (in two decimal places)?"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 91,
   "id": "0dac0d6c",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Coefficient of determination (R^2): 0.15\n"
     ]
    }
   ],
   "source": [
    "print('Coefficient of determination (R^2): %.2f'\n",
    "      % r2_score(y_tes,pred))"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "c266778a",
   "metadata": {},
   "source": [
    "Number 17:\n",
    "Obtain the feature weights from your linear model above. Which features have the lowest and highest weights respectively?"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 92,
   "id": "325fae43",
   "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>Weight</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>RH_2</th>\n",
       "      <td>-0.456698</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>T_out</th>\n",
       "      <td>-0.321860</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>T2</th>\n",
       "      <td>-0.236178</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>T9</th>\n",
       "      <td>-0.189941</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>RH_8</th>\n",
       "      <td>-0.157595</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>RH_out</th>\n",
       "      <td>-0.077671</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>RH_7</th>\n",
       "      <td>-0.044614</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>RH_9</th>\n",
       "      <td>-0.039800</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>T5</th>\n",
       "      <td>-0.015657</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>T1</th>\n",
       "      <td>-0.003281</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>rv2</th>\n",
       "      <td>0.000770</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>rv1</th>\n",
       "      <td>0.000770</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Press_mm_hg</th>\n",
       "      <td>0.006839</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>T7</th>\n",
       "      <td>0.010319</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Visibility</th>\n",
       "      <td>0.012307</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>RH_5</th>\n",
       "      <td>0.016006</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>RH_4</th>\n",
       "      <td>0.026386</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>T4</th>\n",
       "      <td>0.028981</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Windspeed</th>\n",
       "      <td>0.029183</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>RH_6</th>\n",
       "      <td>0.038049</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>RH_3</th>\n",
       "      <td>0.096048</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>T8</th>\n",
       "      <td>0.101995</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Tdewpoint</th>\n",
       "      <td>0.117758</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>T6</th>\n",
       "      <td>0.236425</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>T3</th>\n",
       "      <td>0.290627</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>RH_1</th>\n",
       "      <td>0.553547</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "               Weight\n",
       "RH_2        -0.456698\n",
       "T_out       -0.321860\n",
       "T2          -0.236178\n",
       "T9          -0.189941\n",
       "RH_8        -0.157595\n",
       "RH_out      -0.077671\n",
       "RH_7        -0.044614\n",
       "RH_9        -0.039800\n",
       "T5          -0.015657\n",
       "T1          -0.003281\n",
       "rv2          0.000770\n",
       "rv1          0.000770\n",
       "Press_mm_hg  0.006839\n",
       "T7           0.010319\n",
       "Visibility   0.012307\n",
       "RH_5         0.016006\n",
       "RH_4         0.026386\n",
       "T4           0.028981\n",
       "Windspeed    0.029183\n",
       "RH_6         0.038049\n",
       "RH_3         0.096048\n",
       "T8           0.101995\n",
       "Tdewpoint    0.117758\n",
       "T6           0.236425\n",
       "T3           0.290627\n",
       "RH_1         0.553547"
      ]
     },
     "execution_count": 92,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "def feature_weights(model, features):\n",
    "    weights = pd.Series(model.coef_, features).sort_values()\n",
    "    weights_df = pd.DataFrame(weights, columns=['Weight'])\n",
    "    return weights_df\n",
    "\n",
    "\n",
    "feature = features.columns\n",
    "feature_weights( linear, feature)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "8ad3f483",
   "metadata": {},
   "source": [
    "Number 18:\n",
    "Train a ridge regression model with an alpha value of 0.4.\n",
    "Is there any change to the root mean squared error (RMSE) when evaluated on the test set?"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 93,
   "id": "e32c645c",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.088"
      ]
     },
     "execution_count": 93,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "ridge_reg = Ridge(alpha=0.4)\n",
    "ridge_reg.fit(x_tra, y_tra)\n",
    "ridge_pred = ridge_reg.predict(x_tes)\n",
    "\n",
    "#obtaining the RMSE score\n",
    "rmse = np.sqrt(mean_squared_error(y_tes, ridge_pred))\n",
    "round(rmse, 3)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "59cf444b",
   "metadata": {},
   "source": [
    "Number 19:\n",
    "Train a lasso regression model with an alpha value of 0.001 and obtain the new feature weights with it.\n",
    "How many of the features have non-zero feature weights?"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 94,
   "id": "cffe23a9",
   "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>Weight</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>RH_out</th>\n",
       "      <td>-0.049557</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>RH_8</th>\n",
       "      <td>-0.000110</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>T1</th>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Tdewpoint</th>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Visibility</th>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Press_mm_hg</th>\n",
       "      <td>-0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>T_out</th>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>RH_9</th>\n",
       "      <td>-0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>T9</th>\n",
       "      <td>-0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>T8</th>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>RH_7</th>\n",
       "      <td>-0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>rv1</th>\n",
       "      <td>-0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>T7</th>\n",
       "      <td>-0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>T6</th>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>RH_5</th>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>T5</th>\n",
       "      <td>-0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>RH_4</th>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>T4</th>\n",
       "      <td>-0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>RH_3</th>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>T3</th>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>RH_2</th>\n",
       "      <td>-0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>T2</th>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>RH_6</th>\n",
       "      <td>-0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>rv2</th>\n",
       "      <td>-0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Windspeed</th>\n",
       "      <td>0.002912</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>RH_1</th>\n",
       "      <td>0.017880</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "               Weight\n",
       "RH_out      -0.049557\n",
       "RH_8        -0.000110\n",
       "T1           0.000000\n",
       "Tdewpoint    0.000000\n",
       "Visibility   0.000000\n",
       "Press_mm_hg -0.000000\n",
       "T_out        0.000000\n",
       "RH_9        -0.000000\n",
       "T9          -0.000000\n",
       "T8           0.000000\n",
       "RH_7        -0.000000\n",
       "rv1         -0.000000\n",
       "T7          -0.000000\n",
       "T6           0.000000\n",
       "RH_5         0.000000\n",
       "T5          -0.000000\n",
       "RH_4         0.000000\n",
       "T4          -0.000000\n",
       "RH_3         0.000000\n",
       "T3           0.000000\n",
       "RH_2        -0.000000\n",
       "T2           0.000000\n",
       "RH_6        -0.000000\n",
       "rv2         -0.000000\n",
       "Windspeed    0.002912\n",
       "RH_1         0.017880"
      ]
     },
     "execution_count": 94,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "lasso_reg = Lasso(alpha=0.001)\n",
    "lasso_reg.fit(x_tra, y_tra)\n",
    "\n",
    "feature_weights(lasso_reg, feature)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "9259f2df",
   "metadata": {},
   "source": [
    "Number 20:\n",
    "What is the new RMSE with the lasso regression? (Answer should be in three (3) decimal places)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 95,
   "id": "f377ff5c",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Lasso RMSE: 0.094\n"
     ]
    }
   ],
   "source": [
    "lasso_pred=lasso_reg.predict(x_tes)\n",
    "rmse=np.sqrt(mean_squared_error(y_tes,lasso_pred))\n",
    "print(f'Lasso RMSE: {round(rmse,3)}')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "628b6326",
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3 (ipykernel)",
   "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": "3.9.7"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 5
}