Stage2.ipynb

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      "name": "Stage2.ipynb",
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      "cell_type": "code",
      "execution_count": 1,
      "metadata": {
        "id": "VcnBinB73JV_"
      },
      "outputs": [],
      "source": [
        "import pandas as pd \n",
        "import numpy as np\n",
        "import seaborn as sns\n",
        "import matplotlib.pyplot as plt"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# read the data\n",
        "df = pd.read_csv('/content/drive/MyDrive/Hamoye/energydata_complete.csv')\n",
        "df.shape"
      ],
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        "colab": {
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        "outputId": "c354d93c-f13a-4602-e5fa-9ea01d64fcca"
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      "execution_count": 2,
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          "data": {
            "text/plain": [
              "(19735, 29)"
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          "metadata": {},
          "execution_count": 2
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      "cell_type": "code",
      "source": [
        "df.head()"
      ],
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          "height": 444
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      "execution_count": 3,
      "outputs": [
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          "data": {
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              "<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>RH_4</th>\n",
              "      <th>T5</th>\n",
              "      <th>RH_5</th>\n",
              "      <th>T6</th>\n",
              "      <th>RH_6</th>\n",
              "      <th>T7</th>\n",
              "      <th>RH_7</th>\n",
              "      <th>T8</th>\n",
              "      <th>RH_8</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>45.566667</td>\n",
              "      <td>17.166667</td>\n",
              "      <td>55.20</td>\n",
              "      <td>7.026667</td>\n",
              "      <td>84.256667</td>\n",
              "      <td>17.200000</td>\n",
              "      <td>41.626667</td>\n",
              "      <td>18.2</td>\n",
              "      <td>48.900000</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>45.992500</td>\n",
              "      <td>17.166667</td>\n",
              "      <td>55.20</td>\n",
              "      <td>6.833333</td>\n",
              "      <td>84.063333</td>\n",
              "      <td>17.200000</td>\n",
              "      <td>41.560000</td>\n",
              "      <td>18.2</td>\n",
              "      <td>48.863333</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>45.890000</td>\n",
              "      <td>17.166667</td>\n",
              "      <td>55.09</td>\n",
              "      <td>6.560000</td>\n",
              "      <td>83.156667</td>\n",
              "      <td>17.200000</td>\n",
              "      <td>41.433333</td>\n",
              "      <td>18.2</td>\n",
              "      <td>48.730000</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>45.723333</td>\n",
              "      <td>17.166667</td>\n",
              "      <td>55.09</td>\n",
              "      <td>6.433333</td>\n",
              "      <td>83.423333</td>\n",
              "      <td>17.133333</td>\n",
              "      <td>41.290000</td>\n",
              "      <td>18.1</td>\n",
              "      <td>48.590000</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>45.530000</td>\n",
              "      <td>17.200000</td>\n",
              "      <td>55.09</td>\n",
              "      <td>6.366667</td>\n",
              "      <td>84.893333</td>\n",
              "      <td>17.200000</td>\n",
              "      <td>41.230000</td>\n",
              "      <td>18.1</td>\n",
              "      <td>48.590000</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",
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              "  </tbody>\n",
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            "text/plain": [
              "                  date  Appliances  lights  ...  Tdewpoint        rv1        rv2\n",
              "0  2016-01-11 17:00:00          60      30  ...        5.3  13.275433  13.275433\n",
              "1  2016-01-11 17:10:00          60      30  ...        5.2  18.606195  18.606195\n",
              "2  2016-01-11 17:20:00          50      30  ...        5.1  28.642668  28.642668\n",
              "3  2016-01-11 17:30:00          50      40  ...        5.0  45.410389  45.410389\n",
              "4  2016-01-11 17:40:00          60      40  ...        4.9  10.084097  10.084097\n",
              "\n",
              "[5 rows x 29 columns]"
            ]
          },
          "metadata": {},
          "execution_count": 3
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "df.columns"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "cBDTGsxV3jtF",
        "outputId": "5bde1683-d9f7-48b5-95f3-958d6d9c68c8"
      },
      "execution_count": 4,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Index(['date', 'Appliances', 'lights', 'T1', 'RH_1', 'T2', 'RH_2', 'T3',\n",
              "       'RH_3', 'T4', 'RH_4', 'T5', 'RH_5', 'T6', 'RH_6', 'T7', 'RH_7', 'T8',\n",
              "       'RH_8', 'T9', 'RH_9', 'T_out', 'Press_mm_hg', 'RH_out', 'Windspeed',\n",
              "       'Visibility', 'Tdewpoint', 'rv1', 'rv2'],\n",
              "      dtype='object')"
            ]
          },
          "metadata": {},
          "execution_count": 4
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# check null values\n",
        "obj  = df.isnull().sum()\n",
        "for key,value in obj.iteritems():\n",
        "    print(key,\",\",value)"
      ],
      "metadata": {
        "id": "9-6KmQhB3ohJ",
        "outputId": "e08682ef-0fed-46fd-f3de-409969bb75e0",
        "colab": {
          "base_uri": "https://localhost:8080/"
        }
      },
      "execution_count": 5,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "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"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "X = df.iloc[:,5].values\n",
        "y = df.iloc[:,13].values"
      ],
      "metadata": {
        "id": "dht8FRmMOiyE"
      },
      "execution_count": 6,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "X = X.reshape(-1,1)"
      ],
      "metadata": {
        "id": "w8iThzNRP5fx"
      },
      "execution_count": 7,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "y = y.reshape(-1,1)"
      ],
      "metadata": {
        "id": "x1gEO8osQGGm"
      },
      "execution_count": 8,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "y"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "FHWFsI-eSCO6",
        "outputId": "a577b422-5bdc-465e-8eb0-b4009c5c650a"
      },
      "execution_count": 9,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([[ 7.02666667],\n",
              "       [ 6.83333333],\n",
              "       [ 6.56      ],\n",
              "       ...,\n",
              "       [23.62666667],\n",
              "       [22.43333333],\n",
              "       [21.02666667]])"
            ]
          },
          "metadata": {},
          "execution_count": 9
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "from sklearn.linear_model import LinearRegression\n",
        "regressor = LinearRegression()\n",
        "regressor.fit(X, y)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "KVxR_wsaOgfA",
        "outputId": "70a0d91f-907a-4307-9d7d-ef4b009f07f3"
      },
      "execution_count": 10,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "LinearRegression()"
            ]
          },
          "metadata": {},
          "execution_count": 10
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "\n",
        "from sklearn.metrics import r2_score\n",
        "r2 = r2_score(X,y)\n",
        "print('r2 score for perfect model is', round(r2,2))"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "wwYwwpiLQFSf",
        "outputId": "52c51921-e17c-470c-b8e6-540871540a13"
      },
      "execution_count": 11,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "r2 score for perfect model is -35.39\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "Q13"
      ],
      "metadata": {
        "id": "R-VFRJDWS4A3"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "new_data = df.copy()\n",
        "new_data.drop(['date', 'lights'], axis='columns', inplace=True)"
      ],
      "metadata": {
        "id": "wnod9t0FS3GY"
      },
      "execution_count": 12,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "new_data.head()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 270
        },
        "id": "WrPk0wklT86t",
        "outputId": "9d419b07-fdb4-4980-d278-9f9762300451"
      },
      "execution_count": 13,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "\n",
              "  <div id=\"df-46ed17e4-97bc-49b8-b264-a87586d7f376\">\n",
              "    <div class=\"colab-df-container\">\n",
              "      <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>RH_5</th>\n",
              "      <th>T6</th>\n",
              "      <th>RH_6</th>\n",
              "      <th>T7</th>\n",
              "      <th>RH_7</th>\n",
              "      <th>T8</th>\n",
              "      <th>RH_8</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>55.20</td>\n",
              "      <td>7.026667</td>\n",
              "      <td>84.256667</td>\n",
              "      <td>17.200000</td>\n",
              "      <td>41.626667</td>\n",
              "      <td>18.2</td>\n",
              "      <td>48.900000</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>55.20</td>\n",
              "      <td>6.833333</td>\n",
              "      <td>84.063333</td>\n",
              "      <td>17.200000</td>\n",
              "      <td>41.560000</td>\n",
              "      <td>18.2</td>\n",
              "      <td>48.863333</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>55.09</td>\n",
              "      <td>6.560000</td>\n",
              "      <td>83.156667</td>\n",
              "      <td>17.200000</td>\n",
              "      <td>41.433333</td>\n",
              "      <td>18.2</td>\n",
              "      <td>48.730000</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>55.09</td>\n",
              "      <td>6.433333</td>\n",
              "      <td>83.423333</td>\n",
              "      <td>17.133333</td>\n",
              "      <td>41.290000</td>\n",
              "      <td>18.1</td>\n",
              "      <td>48.590000</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>55.09</td>\n",
              "      <td>6.366667</td>\n",
              "      <td>84.893333</td>\n",
              "      <td>17.200000</td>\n",
              "      <td>41.230000</td>\n",
              "      <td>18.1</td>\n",
              "      <td>48.590000</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",
              "</div>\n",
              "      <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-46ed17e4-97bc-49b8-b264-a87586d7f376')\"\n",
              "              title=\"Convert this dataframe to an interactive table.\"\n",
              "              style=\"display:none;\">\n",
              "        \n",
              "  <svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n",
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              "  </svg>\n",
              "      </button>\n",
              "      \n",
              "  <style>\n",
              "    .colab-df-container {\n",
              "      display:flex;\n",
              "      flex-wrap:wrap;\n",
              "      gap: 12px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert {\n",
              "      background-color: #E8F0FE;\n",
              "      border: none;\n",
              "      border-radius: 50%;\n",
              "      cursor: pointer;\n",
              "      display: none;\n",
              "      fill: #1967D2;\n",
              "      height: 32px;\n",
              "      padding: 0 0 0 0;\n",
              "      width: 32px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert:hover {\n",
              "      background-color: #E2EBFA;\n",
              "      box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "      fill: #174EA6;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert {\n",
              "      background-color: #3B4455;\n",
              "      fill: #D2E3FC;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert:hover {\n",
              "      background-color: #434B5C;\n",
              "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
              "      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
              "      fill: #FFFFFF;\n",
              "    }\n",
              "  </style>\n",
              "\n",
              "      <script>\n",
              "        const buttonEl =\n",
              "          document.querySelector('#df-46ed17e4-97bc-49b8-b264-a87586d7f376 button.colab-df-convert');\n",
              "        buttonEl.style.display =\n",
              "          google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "\n",
              "        async function convertToInteractive(key) {\n",
              "          const element = document.querySelector('#df-46ed17e4-97bc-49b8-b264-a87586d7f376');\n",
              "          const dataTable =\n",
              "            await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                     [key], {});\n",
              "          if (!dataTable) return;\n",
              "\n",
              "          const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "            '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "            + ' to learn more about interactive tables.';\n",
              "          element.innerHTML = '';\n",
              "          dataTable['output_type'] = 'display_data';\n",
              "          await google.colab.output.renderOutput(dataTable, element);\n",
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              "          docLink.innerHTML = docLinkHtml;\n",
              "          element.appendChild(docLink);\n",
              "        }\n",
              "      </script>\n",
              "    </div>\n",
              "  </div>\n",
              "  "
            ],
            "text/plain": [
              "   Appliances     T1       RH_1  ...  Tdewpoint        rv1        rv2\n",
              "0          60  19.89  47.596667  ...        5.3  13.275433  13.275433\n",
              "1          60  19.89  46.693333  ...        5.2  18.606195  18.606195\n",
              "2          50  19.89  46.300000  ...        5.1  28.642668  28.642668\n",
              "3          50  19.89  46.066667  ...        5.0  45.410389  45.410389\n",
              "4          60  19.89  46.333333  ...        4.9  10.084097  10.084097\n",
              "\n",
              "[5 rows x 27 columns]"
            ]
          },
          "metadata": {},
          "execution_count": 13
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "new_data.columns"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "ywkBMLsXVwh-",
        "outputId": "65781ba6-ebd4-4582-e5b5-e04f320b450b"
      },
      "execution_count": 14,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Index(['Appliances', 'T1', 'RH_1', 'T2', 'RH_2', 'T3', 'RH_3', 'T4', 'RH_4',\n",
              "       'T5', 'RH_5', 'T6', 'RH_6', 'T7', 'RH_7', 'T8', 'RH_8', 'T9', 'RH_9',\n",
              "       'T_out', 'Press_mm_hg', 'RH_out', 'Windspeed', 'Visibility',\n",
              "       'Tdewpoint', 'rv1', 'rv2'],\n",
              "      dtype='object')"
            ]
          },
          "metadata": {},
          "execution_count": 14
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "new_X =  new_data.iloc[:,1:].values\n",
        "new_y =  new_data.iloc[:,0].values"
      ],
      "metadata": {
        "id": "t9ea5OdUVnJ9"
      },
      "execution_count": 15,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "new_X.shape"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "qafVz_wBWDP3",
        "outputId": "a6037d52-dccc-4788-8b7a-0cc35bac544b"
      },
      "execution_count": 16,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(19735, 26)"
            ]
          },
          "metadata": {},
          "execution_count": 16
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "new_y.shape"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "hBIltHHxWQnl",
        "outputId": "48030f76-4dd6-41fb-d1c9-5e522ebc58d8"
      },
      "execution_count": 17,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(19735,)"
            ]
          },
          "metadata": {},
          "execution_count": 17
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "from sklearn.model_selection import train_test_split\n",
        "X_train, X_test, y_train, y_test = train_test_split(new_X, new_y, test_size = 0.3, random_state = 42)"
      ],
      "metadata": {
        "id": "kxWSeD-fVFKQ"
      },
      "execution_count": 18,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "from sklearn.preprocessing import MinMaxScaler\n",
        "sc = MinMaxScaler()\n",
        "X_train = sc.fit_transform(X_train)\n",
        "X_test = sc.transform(X_test)"
      ],
      "metadata": {
        "id": "QU0JyamNVQx1"
      },
      "execution_count": 19,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "from sklearn.linear_model import LinearRegression\n",
        "regressor = LinearRegression()\n",
        "regressor.fit(X_train, y_train)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "tYr300S3Wzmb",
        "outputId": "d8fb7134-2e9f-4931-96ec-964e107729a9"
      },
      "execution_count": 20,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "LinearRegression()"
            ]
          },
          "metadata": {},
          "execution_count": 20
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "y_pred = regressor.predict(X_test)"
      ],
      "metadata": {
        "id": "N8JosVIlW2Pa"
      },
      "execution_count": 21,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "from sklearn.metrics import mean_absolute_error\n",
        "round(mean_absolute_error(y_test,y_pred),2)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "m2pcYecCXheq",
        "outputId": "10c208d7-eaaa-49f9-a2cc-4ef843f789bb"
      },
      "execution_count": 22,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "53.64"
            ]
          },
          "metadata": {},
          "execution_count": 22
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "rss = np.sum(np.square(y_test - y_pred))\n",
        "round(rss, 2)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "T6d98q9zYUHH",
        "outputId": "eab00796-e59b-427b-ba62-f6f0d833c4b3"
      },
      "execution_count": 23,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "51918501.21"
            ]
          },
          "metadata": {},
          "execution_count": 23
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "from sklearn.metrics import mean_squared_error\n",
        "rmse = np.sqrt(mean_squared_error(y_test,  y_pred))\n",
        "round(rmse, 3) "
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "7-4K0cqlYkaV",
        "outputId": "853144dd-2158-4be0-b0f2-43c1ef387b26"
      },
      "execution_count": 24,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "93.64"
            ]
          },
          "metadata": {},
          "execution_count": 24
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "\n",
        "r2_score = r2_score(y_test, y_pred)\n",
        "round(r2_score, 2)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "kOMSRVJKZGjt",
        "outputId": "5660d34c-505c-4536-b44e-986ae851a35f"
      },
      "execution_count": 25,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "0.15"
            ]
          },
          "metadata": {},
          "execution_count": 25
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "regressor.coef_"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "qPdmQLo3Zosb",
        "outputId": "b6d5c8f3-daf8-49de-a92d-fe56c031ee5c"
      },
      "execution_count": 26,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([  -3.51072478,  495.52601962, -252.710373  , -469.52136238,\n",
              "        310.97103712,  102.77165244,   31.00966845,   27.31182902,\n",
              "        -16.75282177,   17.03563561,  252.71692835,   40.7120577 ,\n",
              "         11.00293428,  -47.56355153,  109.13469841, -168.62716102,\n",
              "       -203.23662712,  -42.58633878, -344.3898449 ,    7.31524276,\n",
              "        -83.1075989 ,   31.22594824,   13.16807161,  125.43063708,\n",
              "          0.82386956,    0.82386956])"
            ]
          },
          "metadata": {},
          "execution_count": 26
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "np.amin(regressor.coef_, axis=None, out=None)"
      ],
      "metadata": {
        "id": "5cXFrn57adla"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "np.amax(regressor.coef_, axis=None, out=None)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "t2JzfIa3bGF4",
        "outputId": "a2d49651-6c8b-41b1-882a-ed7843c59320"
      },
      "execution_count": 31,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "495.52601961887746"
            ]
          },
          "metadata": {},
          "execution_count": 31
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "new_data.head()"
      ],
      "metadata": {
        "id": "sFU6ow5ha0Dh"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "from sklearn.linear_model import Ridge\n",
        "ridge_reg = Ridge(alpha=0.4)\n",
        "ridge_reg.fit(X_train, y_train)\n"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "fqPbex2cbdRM",
        "outputId": "2499a61e-f633-40dc-a702-790bce44bd5e"
      },
      "execution_count": 34,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Ridge(alpha=0.4)"
            ]
          },
          "metadata": {},
          "execution_count": 34
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "y_ridge_pred = ridge_reg.predict(X_test)"
      ],
      "metadata": {
        "id": "tzc5I2VVbvSF"
      },
      "execution_count": 35,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "\n",
        "rmse_ridge = np.sqrt(mean_squared_error(y_test, y_ridge_pred))\n",
        "round(rmse_ridge, 3) "
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "zhCuyGUDb8-0",
        "outputId": "60283ad5-115d-4755-e568-70bd194208da"
      },
      "execution_count": 36,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "93.66"
            ]
          },
          "metadata": {},
          "execution_count": 36
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "from sklearn.linear_model import Lasso\n",
        "lasso_reg = Lasso(alpha=0.001)\n",
        "lasso_reg.fit(X_train, y_train)\n",
        "y_lasso_pred = lasso_reg.predict(X_test)\n",
        "rmse_lasso = np.sqrt(mean_squared_error(y_test, y_lasso_pred))\n",
        "round(rmse_lasso, 3) "
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "U0BomAyCcgtz",
        "outputId": "e88b0d51-fae2-4ba4-eee2-bf7752aae442"
      },
      "execution_count": 38,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "93.641"
            ]
          },
          "metadata": {},
          "execution_count": 38
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "lasso_reg.coef_"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "rmiTJhxgdIKq",
        "outputId": "2d6750a7-64b1-4f76-b798-f4af1ec0117a"
      },
      "execution_count": 39,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([-4.05585856e+00,  4.94326194e+02, -2.49638653e+02, -4.65821050e+02,\n",
              "        3.10200156e+02,  1.02172472e+02,  3.03442054e+01,  2.65605806e+01,\n",
              "       -1.71130125e+01,  1.70135558e+01,  2.49833620e+02,  4.00909631e+01,\n",
              "        1.03049176e+01, -4.72096100e+01,  1.08879691e+02, -1.68697937e+02,\n",
              "       -2.02480809e+02, -4.24460529e+01, -3.32573565e+02,  7.28122335e+00,\n",
              "       -7.79887791e+01,  3.13880250e+01,  1.30781313e+01,  1.18062594e+02,\n",
              "        1.63099462e+00,  3.77752618e-15])"
            ]
          },
          "metadata": {},
          "execution_count": 39
        }
      ]
    }
  ]
}