Linear Regression House Price Pred Lib PCA.ipynb

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pd.read_csv(\"/content/sample_data/california_housing_train.csv\")"],"metadata":{"id":"lSbJEW6kV4rM"},"execution_count":null,"outputs":[]},{"cell_type":"code","source":["df"],"metadata":{"colab":{"base_uri":"https://localhost:8080/","height":424},"id":"aFsJQnaPWkMc","executionInfo":{"status":"ok","timestamp":1675400914953,"user_tz":-330,"elapsed":21,"user":{"displayName":"Mohit Manghwani","userId":"11157237833313497817"}},"outputId":"c54ab780-e46b-42ef-e4db-45f213cf2ab8"},"execution_count":null,"outputs":[{"output_type":"execute_result","data":{"text/plain":["       longitude  latitude  housing_median_age  total_rooms  total_bedrooms  \\\n","0        -114.31     34.19                15.0       5612.0          1283.0   \n","1        -114.47     34.40                19.0       7650.0          1901.0   \n","2        -114.56     33.69                17.0        720.0           174.0   \n","3        -114.57     33.64                14.0       1501.0           337.0   \n","4        -114.57     33.57                20.0       1454.0           326.0   \n","...          ...       ...                 ...          ...             ...   \n","16995    -124.26     40.58                52.0       2217.0           394.0   \n","16996    -124.27     40.69                36.0       2349.0           528.0   \n","16997    -124.30     41.84                17.0       2677.0           531.0   \n","16998    -124.30     41.80                19.0       2672.0           552.0   \n","16999    -124.35     40.54                52.0       1820.0           300.0   \n","\n","       population  households  median_income  median_house_value  \n","0          1015.0       472.0         1.4936             66900.0  \n","1          1129.0       463.0         1.8200             80100.0  \n","2           333.0       117.0         1.6509             85700.0  \n","3           515.0       226.0         3.1917             73400.0  \n","4           624.0       262.0         1.9250             65500.0  \n","...           ...         ...            ...                 ...  \n","16995       907.0       369.0         2.3571            111400.0  \n","16996      1194.0       465.0         2.5179             79000.0  \n","16997      1244.0       456.0         3.0313            103600.0  \n","16998      1298.0       478.0         1.9797             85800.0  \n","16999       806.0       270.0         3.0147             94600.0  \n","\n","[17000 rows x 9 columns]"],"text/html":["\n","  <div id=\"df-2ba118d2-fb96-4a1a-a949-d6983e2ed90a\">\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>longitude</th>\n","      <th>latitude</th>\n","      <th>housing_median_age</th>\n","      <th>total_rooms</th>\n","      <th>total_bedrooms</th>\n","      <th>population</th>\n","      <th>households</th>\n","      <th>median_income</th>\n","      <th>median_house_value</th>\n","    </tr>\n","  </thead>\n","  <tbody>\n","    <tr>\n","      <th>0</th>\n","      <td>-114.31</td>\n","      <td>34.19</td>\n","      <td>15.0</td>\n","      <td>5612.0</td>\n","      <td>1283.0</td>\n","      <td>1015.0</td>\n","      <td>472.0</td>\n","      <td>1.4936</td>\n","      <td>66900.0</td>\n","    </tr>\n","    <tr>\n","      <th>1</th>\n","      <td>-114.47</td>\n","      <td>34.40</td>\n","      <td>19.0</td>\n","      <td>7650.0</td>\n","      <td>1901.0</td>\n","      <td>1129.0</td>\n","      <td>463.0</td>\n","      <td>1.8200</td>\n","      <td>80100.0</td>\n","    </tr>\n","    <tr>\n","      <th>2</th>\n","      <td>-114.56</td>\n","      <td>33.69</td>\n","      <td>17.0</td>\n","      <td>720.0</td>\n","      <td>174.0</td>\n","      <td>333.0</td>\n","      <td>117.0</td>\n","      <td>1.6509</td>\n","      <td>85700.0</td>\n","    </tr>\n","    <tr>\n","      <th>3</th>\n","      <td>-114.57</td>\n","      <td>33.64</td>\n","      <td>14.0</td>\n","      <td>1501.0</td>\n","      <td>337.0</td>\n","      <td>515.0</td>\n","      <td>226.0</td>\n","      <td>3.1917</td>\n","      <td>73400.0</td>\n","    </tr>\n","    <tr>\n","      <th>4</th>\n","      <td>-114.57</td>\n","      <td>33.57</td>\n","      <td>20.0</td>\n","      <td>1454.0</td>\n","      <td>326.0</td>\n","      <td>624.0</td>\n","      <td>262.0</td>\n","      <td>1.9250</td>\n","      <td>65500.0</td>\n","    </tr>\n","    <tr>\n","      <th>...</th>\n","      <td>...</td>\n","      <td>...</td>\n","      <td>...</td>\n","      <td>...</td>\n","      <td>...</td>\n","      <td>...</td>\n","      <td>...</td>\n","      <td>...</td>\n","      <td>...</td>\n","    </tr>\n","    <tr>\n","      <th>16995</th>\n","      <td>-124.26</td>\n","      <td>40.58</td>\n","      <td>52.0</td>\n","      <td>2217.0</td>\n","      <td>394.0</td>\n","      <td>907.0</td>\n","      <td>369.0</td>\n","      <td>2.3571</td>\n","      <td>111400.0</td>\n","    </tr>\n","    <tr>\n","      <th>16996</th>\n","      <td>-124.27</td>\n","      <td>40.69</td>\n","      <td>36.0</td>\n","      <td>2349.0</td>\n","      <td>528.0</td>\n","      <td>1194.0</td>\n","      <td>465.0</td>\n","      <td>2.5179</td>\n","      <td>79000.0</td>\n","    </tr>\n","    <tr>\n","      <th>16997</th>\n","      <td>-124.30</td>\n","      <td>41.84</td>\n","      <td>17.0</td>\n","      <td>2677.0</td>\n","      <td>531.0</td>\n","      <td>1244.0</td>\n","      <td>456.0</td>\n","      <td>3.0313</td>\n","      <td>103600.0</td>\n","    </tr>\n","    <tr>\n","      <th>16998</th>\n","      <td>-124.30</td>\n","      <td>41.80</td>\n","      <td>19.0</td>\n","      <td>2672.0</td>\n","      <td>552.0</td>\n","      <td>1298.0</td>\n","      <td>478.0</td>\n","      <td>1.9797</td>\n","      <td>85800.0</td>\n","    </tr>\n","    <tr>\n","      <th>16999</th>\n","      <td>-124.35</td>\n","      <td>40.54</td>\n","      <td>52.0</td>\n","      <td>1820.0</td>\n","      <td>300.0</td>\n","      <td>806.0</td>\n","      <td>270.0</td>\n","      <td>3.0147</td>\n","      <td>94600.0</td>\n","    </tr>\n","  </tbody>\n","</table>\n","<p>17000 rows × 9 columns</p>\n","</div>\n","      <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-2ba118d2-fb96-4a1a-a949-d6983e2ed90a')\"\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","       width=\"24px\">\n","    <path d=\"M0 0h24v24H0V0z\" fill=\"none\"/>\n","    <path d=\"M18.56 5.44l.94 2.06.94-2.06 2.06-.94-2.06-.94-.94-2.06-.94 2.06-2.06.94zm-11 1L8.5 8.5l.94-2.06 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size"],"metadata":{"id":"PPIUBqCbjg7f"}},{"cell_type":"code","source":["df.isna().sum()"],"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"eVfMMqLaa1ib","executionInfo":{"status":"ok","timestamp":1675400914954,"user_tz":-330,"elapsed":20,"user":{"displayName":"Mohit Manghwani","userId":"11157237833313497817"}},"outputId":"ace5a152-cb0b-4368-a11d-cdb7cacc2cf1"},"execution_count":null,"outputs":[{"output_type":"execute_result","data":{"text/plain":["longitude             0\n","latitude              0\n","housing_median_age    0\n","total_rooms           0\n","total_bedrooms        0\n","population            0\n","households            0\n","median_income         0\n","median_house_value    0\n","dtype: int64"]},"metadata":{},"execution_count":52}]},{"cell_type":"markdown","source":["input <- input features or independent variables  \n","output <- target features or dependent variables\n","\n","covariance is the variance for the case when two random variables co-exist together.\n","\n","::"],"metadata":{"id":"Os4V9JTdnVRO"}},{"cell_type":"code","source":["x = np.random.normal(loc=0,scale=1,size=50)\n","y = (2*x) + 5 + np.random.normal(loc=0,scale=3,size=50)"],"metadata":{"id":"_wCIb455e2Dy"},"execution_count":null,"outputs":[]},{"cell_type":"code","source":["fake = pd.DataFrame(data={\"X\":x,\"Y\":y})"],"metadata":{"id":"WtkZJ3UYyAcD"},"execution_count":null,"outputs":[]},{"cell_type":"code","source":["fake"],"metadata":{"colab":{"base_uri":"https://localhost:8080/","height":1000},"id":"ouuM4WVwzWxA","executionInfo":{"status":"ok","timestamp":1675400914955,"user_tz":-330,"elapsed":19,"user":{"displayName":"Mohit Manghwani","userId":"11157237833313497817"}},"outputId":"e2e84a31-eb73-4640-c58d-03277819c93a"},"execution_count":null,"outputs":[{"output_type":"execute_result","data":{"text/plain":["           X          Y\n","0  -0.228533   0.567305\n","1   2.119458   7.421301\n","2   0.335803   2.081918\n","3  -0.811790   2.229171\n","4   0.613413   2.090928\n","5  -1.758312   2.341745\n","6   0.255484   4.662908\n","7  -0.284276   1.211108\n","8   0.562834  10.529592\n","9  -1.908022   5.980169\n","10  1.701257   5.158821\n","11  0.370567   4.987773\n","12  0.597196   9.132933\n","13 -1.187789   9.196135\n","14  1.114862   4.562791\n","15  0.267834   6.886241\n","16  0.148932   2.027250\n","17  0.488261   0.418837\n","18 -0.797004   4.316353\n","19 -0.443446   7.817820\n","20  1.285092   3.404528\n","21 -1.413854   3.236885\n","22  0.372111   7.775607\n","23 -0.053615   4.795356\n","24 -0.819172   5.399505\n","25 -0.163908   3.798283\n","26  1.369649   7.348937\n","27  0.657811  11.473975\n","28  0.569534   3.472975\n","29  0.092641   3.480912\n","30  1.685536   5.392115\n","31  0.647868   8.682542\n","32  2.092838   6.765514\n","33 -1.122962   3.150466\n","34 -1.212269   1.828210\n","35 -0.245981   4.601737\n","36  1.393830   7.948249\n","37  0.600744   3.352304\n","38  0.739744   3.828125\n","39 -0.359031   4.020003\n","40 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 <td>0.092641</td>\n","      <td>3.480912</td>\n","    </tr>\n","    <tr>\n","      <th>30</th>\n","      <td>1.685536</td>\n","      <td>5.392115</td>\n","    </tr>\n","    <tr>\n","      <th>31</th>\n","      <td>0.647868</td>\n","      <td>8.682542</td>\n","    </tr>\n","    <tr>\n","      <th>32</th>\n","      <td>2.092838</td>\n","      <td>6.765514</td>\n","    </tr>\n","    <tr>\n","      <th>33</th>\n","      <td>-1.122962</td>\n","      <td>3.150466</td>\n","    </tr>\n","    <tr>\n","      <th>34</th>\n","      <td>-1.212269</td>\n","      <td>1.828210</td>\n","    </tr>\n","    <tr>\n","      <th>35</th>\n","      <td>-0.245981</td>\n","      <td>4.601737</td>\n","    </tr>\n","    <tr>\n","      <th>36</th>\n","      <td>1.393830</td>\n","      <td>7.948249</td>\n","    </tr>\n","    <tr>\n","      <th>37</th>\n","      <td>0.600744</td>\n","      <td>3.352304</td>\n","    </tr>\n","    <tr>\n","      <th>38</th>\n","      <td>0.739744</td>\n","      <td>3.828125</td>\n","    </tr>\n","    <tr>\n","      <th>39</th>\n","      <td>-0.359031</td>\n","      <td>4.020003</td>\n","    </tr>\n","    <tr>\n","      <th>40</th>\n","      <td>-0.388728</td>\n","      <td>5.930065</td>\n","    </tr>\n","    <tr>\n","      <th>41</th>\n","      <td>1.367524</td>\n","      <td>12.965307</td>\n","    </tr>\n","    <tr>\n","      <th>42</th>\n","      <td>0.007886</td>\n","      <td>4.574458</td>\n","    </tr>\n","    <tr>\n","      <th>43</th>\n","      <td>-1.007549</td>\n","      <td>1.571961</td>\n","    </tr>\n","    <tr>\n","      <th>44</th>\n","      <td>1.450056</td>\n","      <td>9.684048</td>\n","    </tr>\n","    <tr>\n","      <th>45</th>\n","      <td>-0.317859</td>\n","      <td>5.438131</td>\n","    </tr>\n","    <tr>\n","      <th>46</th>\n","      <td>-1.129785</td>\n","      <td>1.867356</td>\n","    </tr>\n","    <tr>\n","      <th>47</th>\n","      <td>0.356024</td>\n","      <td>1.261390</td>\n","    </tr>\n","    <tr>\n","      <th>48</th>\n","      <td>-0.493128</td>\n","      <td>1.601301</td>\n","    </tr>\n","    <tr>\n","      <th>49</th>\n","      <td>-0.408895</td>\n","      <td>7.402742</td>\n","    </tr>\n","  </tbody>\n","</table>\n","</div>\n","      <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-19f7e64e-cdca-4161-96f2-4aecbb687c51')\"\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","       width=\"24px\">\n","    <path d=\"M0 0h24v24H0V0z\" fill=\"none\"/>\n","    <path d=\"M18.56 5.44l.94 2.06.94-2.06 2.06-.94-2.06-.94-.94-2.06-.94 2.06-2.06.94zm-11 1L8.5 8.5l.94-2.06 2.06-.94-2.06-.94L8.5 2.5l-.94 2.06-2.06.94zm10 10l.94 2.06.94-2.06 2.06-.94-2.06-.94-.94-2.06-.94 2.06-2.06.94z\"/><path d=\"M17.41 7.96l-1.37-1.37c-.4-.4-.92-.59-1.43-.59-.52 0-1.04.2-1.43.59L10.3 9.45l-7.72 7.72c-.78.78-.78 2.05 0 2.83L4 21.41c.39.39.9.59 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\n"},"metadata":{"needs_background":"light"}}]},{"cell_type":"code","source":["cov = np.cov(x,y)"],"metadata":{"id":"v6EXfHVKzpEW"},"execution_count":null,"outputs":[]},{"cell_type":"code","source":["plt.plot(cov)"],"metadata":{"colab":{"base_uri":"https://localhost:8080/","height":300},"id":"DtTF0Ux315R6","executionInfo":{"status":"ok","timestamp":1675400915819,"user_tz":-330,"elapsed":880,"user":{"displayName":"Mohit Manghwani","userId":"11157237833313497817"}},"outputId":"a9469131-563e-4dfc-c481-fcde68af069b"},"execution_count":null,"outputs":[{"output_type":"execute_result","data":{"text/plain":["[<matplotlib.lines.Line2D at 0x7f993bbfca00>,\n"," <matplotlib.lines.Line2D at 0x7f993bbfc910>]"]},"metadata":{},"execution_count":58},{"output_type":"display_data","data":{"text/plain":["<Figure size 432x288 with 1 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column"],"metadata":{"id":"W-y1CLXBzRLx"}},{"cell_type":"code","source":["df.isna().sum()"],"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"clM5Mrrzz7p1","executionInfo":{"status":"ok","timestamp":1675400915820,"user_tz":-330,"elapsed":49,"user":{"displayName":"Mohit Manghwani","userId":"11157237833313497817"}},"outputId":"605d659e-1f33-45b0-aecc-5f227fb181f2"},"execution_count":null,"outputs":[{"output_type":"execute_result","data":{"text/plain":["longitude             0\n","latitude              0\n","housing_median_age    0\n","total_rooms           0\n","total_bedrooms        0\n","population            0\n","households            0\n","median_income         0\n","median_house_value    0\n","dtype: int64"]},"metadata":{},"execution_count":61}]},{"cell_type":"code","source":["question  = df.iloc[:,:-1]\n","answer = df.iloc[:,-1]\n","answer"],"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"s6lDqaaLz-B9","executionInfo":{"status":"ok","timestamp":1675400915821,"user_tz":-330,"elapsed":47,"user":{"displayName":"Mohit Manghwani","userId":"11157237833313497817"}},"outputId":"5a24cf11-6cc9-4bac-8b04-b117cef21334"},"execution_count":null,"outputs":[{"output_type":"execute_result","data":{"text/plain":["0         66900.0\n","1         80100.0\n","2         85700.0\n","3         73400.0\n","4         65500.0\n","           ...   \n","16995    111400.0\n","16996     79000.0\n","16997    103600.0\n","16998     85800.0\n","16999     94600.0\n","Name: median_house_value, Length: 17000, dtype: float64"]},"metadata":{},"execution_count":62}]},{"cell_type":"code","source":["question"],"metadata":{"colab":{"base_uri":"https://localhost:8080/","height":424},"id":"jf-mX2uG3E9c","executionInfo":{"status":"ok","timestamp":1675400915821,"user_tz":-330,"elapsed":46,"user":{"displayName":"Mohit 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  531.0   \n","16998    -124.30     41.80                19.0       2672.0           552.0   \n","16999    -124.35     40.54                52.0       1820.0           300.0   \n","\n","       population  households  median_income  \n","0          1015.0       472.0         1.4936  \n","1          1129.0       463.0         1.8200  \n","2           333.0       117.0         1.6509  \n","3           515.0       226.0         3.1917  \n","4           624.0       262.0         1.9250  \n","...           ...         ...            ...  \n","16995       907.0       369.0         2.3571  \n","16996      1194.0       465.0         2.5179  \n","16997      1244.0       456.0         3.0313  \n","16998      1298.0       478.0         1.9797  \n","16999       806.0       270.0         3.0147  \n","\n","[17000 rows x 8 columns]"],"text/html":["\n","  <div id=\"df-76c96fcd-c4f3-4593-9997-c7397f20a4de\">\n","    <div class=\"colab-df-container\">\n","      <div>\n","<style scoped>\n","    .dataframe 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<td>34.40</td>\n","      <td>19.0</td>\n","      <td>7650.0</td>\n","      <td>1901.0</td>\n","      <td>1129.0</td>\n","      <td>463.0</td>\n","      <td>1.8200</td>\n","    </tr>\n","    <tr>\n","      <th>2</th>\n","      <td>-114.56</td>\n","      <td>33.69</td>\n","      <td>17.0</td>\n","      <td>720.0</td>\n","      <td>174.0</td>\n","      <td>333.0</td>\n","      <td>117.0</td>\n","      <td>1.6509</td>\n","    </tr>\n","    <tr>\n","      <th>3</th>\n","      <td>-114.57</td>\n","      <td>33.64</td>\n","      <td>14.0</td>\n","      <td>1501.0</td>\n","      <td>337.0</td>\n","      <td>515.0</td>\n","      <td>226.0</td>\n","      <td>3.1917</td>\n","    </tr>\n","    <tr>\n","      <th>4</th>\n","      <td>-114.57</td>\n","      <td>33.57</td>\n","      <td>20.0</td>\n","      <td>1454.0</td>\n","      <td>326.0</td>\n","      <td>624.0</td>\n","      <td>262.0</td>\n","      <td>1.9250</td>\n","    </tr>\n","    <tr>\n","      <th>...</th>\n","      <td>...</td>\n","      <td>...</td>\n","      <td>...</td>\n","      <td>...</td>\n","      <td>...</td>\n","      <td>...</td>\n","      <td>...</td>\n","      <td>...</td>\n","    </tr>\n","    <tr>\n","      <th>16995</th>\n","      <td>-124.26</td>\n","      <td>40.58</td>\n","      <td>52.0</td>\n","      <td>2217.0</td>\n","      <td>394.0</td>\n","      <td>907.0</td>\n","      <td>369.0</td>\n","      <td>2.3571</td>\n","    </tr>\n","    <tr>\n","      <th>16996</th>\n","      <td>-124.27</td>\n","      <td>40.69</td>\n","      <td>36.0</td>\n","      <td>2349.0</td>\n","      <td>528.0</td>\n","      <td>1194.0</td>\n","      <td>465.0</td>\n","      <td>2.5179</td>\n","    </tr>\n","    <tr>\n","      <th>16997</th>\n","      <td>-124.30</td>\n","      <td>41.84</td>\n","      <td>17.0</td>\n","      <td>2677.0</td>\n","      <td>531.0</td>\n","      <td>1244.0</td>\n","      <td>456.0</td>\n","      <td>3.0313</td>\n","    </tr>\n","    <tr>\n","      <th>16998</th>\n","      <td>-124.30</td>\n","      <td>41.80</td>\n","      <td>19.0</td>\n","      <td>2672.0</td>\n","      <td>552.0</td>\n","      <td>1298.0</td>\n","      <td>478.0</td>\n","      <td>1.9797</td>\n","    </tr>\n","    <tr>\n","      <th>16999</th>\n","      <td>-124.35</td>\n","      <td>40.54</td>\n","      <td>52.0</td>\n","      <td>1820.0</td>\n","      <td>300.0</td>\n","      <td>806.0</td>\n","      <td>270.0</td>\n","      <td>3.0147</td>\n","    </tr>\n","  </tbody>\n","</table>\n","<p>17000 rows × 8 columns</p>\n","</div>\n","      <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-76c96fcd-c4f3-4593-9997-c7397f20a4de')\"\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","       width=\"24px\">\n","    <path d=\"M0 0h24v24H0V0z\" fill=\"none\"/>\n","    <path d=\"M18.56 5.44l.94 2.06.94-2.06 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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","          const docLink = document.createElement('div');\n","          docLink.innerHTML = docLinkHtml;\n","          element.appendChild(docLink);\n","        }\n","      </script>\n","    </div>\n","  </div>\n","  "]},"metadata":{},"execution_count":63}]},{"cell_type":"code","source":["from sklearn.linear_model import LinearRegression"],"metadata":{"id":"A8Ut_-WN3JgE"},"execution_count":null,"outputs":[]},{"cell_type":"code","source":["algo = LinearRegression()"],"metadata":{"id":"tzjvwxjB4pfr"},"execution_count":null,"outputs":[]},{"cell_type":"code","source":["algo.fit(question,answer)"],"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"ow7e_KVh8Rb6","executionInfo":{"status":"ok","timestamp":1675400915822,"user_tz":-330,"elapsed":45,"user":{"displayName":"Mohit Manghwani","userId":"11157237833313497817"}},"outputId":"771cfc87-6596-4802-a3b0-0f79e7b1380c"},"execution_count":null,"outputs":[{"output_type":"execute_result","data":{"text/plain":["LinearRegression()"]},"metadata":{},"execution_count":66}]},{"cell_type":"code","source":["predictions = algo.predict(question)\n","predictions"],"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"mBVP3ffh8gTJ","executionInfo":{"status":"ok","timestamp":1675400915823,"user_tz":-330,"elapsed":44,"user":{"displayName":"Mohit 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Manghwani","userId":"11157237833313497817"}},"outputId":"f29435c7-223f-49db-f039-6e737745629f"},"execution_count":null,"outputs":[{"output_type":"execute_result","data":{"text/plain":["                    longitude  latitude  housing_median_age  total_rooms  \\\n","longitude            1.000000 -0.925208           -0.114250     0.047010   \n","latitude            -0.925208  1.000000            0.016454    -0.038773   \n","housing_median_age  -0.114250  0.016454            1.000000    -0.360984   \n","total_rooms          0.047010 -0.038773           -0.360984     1.000000   \n","total_bedrooms       0.071802 -0.069373           -0.320434     0.928403   \n","population           0.101674 -0.111261           -0.295890     0.860170   \n","households           0.059628 -0.074902           -0.302754     0.919018   \n","median_income       -0.015485 -0.080303           -0.115932     0.195383   \n","median_house_value  -0.044982 -0.144917            0.106758     0.130991   \n","\n","                    total_bedrooms  population  households  median_income  \\\n","longitude                 0.071802    0.101674    0.059628      -0.015485   \n","latitude                 -0.069373   -0.111261   -0.074902      -0.080303   \n","housing_median_age       -0.320434   -0.295890   -0.302754      -0.115932   \n","total_rooms               0.928403    0.860170    0.919018       0.195383   \n","total_bedrooms            1.000000    0.881169    0.980920      -0.013495   \n","population                0.881169    1.000000    0.909247      -0.000638   \n","households                0.980920    0.909247    1.000000       0.007644   \n","median_income            -0.013495   -0.000638    0.007644       1.000000   \n","median_house_value        0.045783   -0.027850    0.061031       0.691871   \n","\n","                    median_house_value  \n","longitude                    -0.044982  \n","latitude                     -0.144917  \n","housing_median_age            0.106758  \n","total_rooms                   0.130991  \n","total_bedrooms                0.045783  \n","population                   -0.027850  \n","households                    0.061031  \n","median_income                 0.691871  \n","median_house_value            1.000000  "],"text/html":["\n","  <div id=\"df-f6898ee3-6640-4a92-a101-46b160ca7cce\">\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>longitude</th>\n","      <th>latitude</th>\n","      <th>housing_median_age</th>\n","      <th>total_rooms</th>\n","      <th>total_bedrooms</th>\n","      <th>population</th>\n","      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<td>-0.302754</td>\n","      <td>-0.115932</td>\n","      <td>0.106758</td>\n","    </tr>\n","    <tr>\n","      <th>total_rooms</th>\n","      <td>0.047010</td>\n","      <td>-0.038773</td>\n","      <td>-0.360984</td>\n","      <td>1.000000</td>\n","      <td>0.928403</td>\n","      <td>0.860170</td>\n","      <td>0.919018</td>\n","      <td>0.195383</td>\n","      <td>0.130991</td>\n","    </tr>\n","    <tr>\n","      <th>total_bedrooms</th>\n","      <td>0.071802</td>\n","      <td>-0.069373</td>\n","      <td>-0.320434</td>\n","      <td>0.928403</td>\n","      <td>1.000000</td>\n","      <td>0.881169</td>\n","      <td>0.980920</td>\n","      <td>-0.013495</td>\n","      <td>0.045783</td>\n","    </tr>\n","    <tr>\n","      <th>population</th>\n","      <td>0.101674</td>\n","      <td>-0.111261</td>\n","      <td>-0.295890</td>\n","      <td>0.860170</td>\n","      <td>0.881169</td>\n","      <td>1.000000</td>\n","      <td>0.909247</td>\n","      <td>-0.000638</td>\n","      <td>-0.027850</td>\n","    </tr>\n","    <tr>\n","      <th>households</th>\n","      <td>0.059628</td>\n","      <td>-0.074902</td>\n","      <td>-0.302754</td>\n","      <td>0.919018</td>\n","      <td>0.980920</td>\n","      <td>0.909247</td>\n","      <td>1.000000</td>\n","      <td>0.007644</td>\n","      <td>0.061031</td>\n","    </tr>\n","    <tr>\n","      <th>median_income</th>\n","      <td>-0.015485</td>\n","      <td>-0.080303</td>\n","      <td>-0.115932</td>\n","      <td>0.195383</td>\n","      <td>-0.013495</td>\n","      <td>-0.000638</td>\n","      <td>0.007644</td>\n","      <td>1.000000</td>\n","      <td>0.691871</td>\n","    </tr>\n","    <tr>\n","      <th>median_house_value</th>\n","      <td>-0.044982</td>\n","      <td>-0.144917</td>\n","      <td>0.106758</td>\n","      <td>0.130991</td>\n","      <td>0.045783</td>\n","      <td>-0.027850</td>\n","      <td>0.061031</td>\n","      <td>0.691871</td>\n","      <td>1.000000</td>\n","    </tr>\n","  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a[a==1]"],"metadata":{"id":"tqqMeXbnUg0S"},"execution_count":null,"outputs":[]},{"cell_type":"code","source":["b = df[a.index]"],"metadata":{"id":"NrnPPdHmWhIj"},"execution_count":null,"outputs":[]},{"cell_type":"code","source":["b"],"metadata":{"colab":{"base_uri":"https://localhost:8080/","height":424},"id":"OOyVkg-xXp2E","executionInfo":{"status":"ok","timestamp":1675400915827,"user_tz":-330,"elapsed":41,"user":{"displayName":"Mohit Manghwani","userId":"11157237833313497817"}},"outputId":"5d2f4f40-33e1-4399-9135-ca030ef20cfa"},"execution_count":null,"outputs":[{"output_type":"execute_result","data":{"text/plain":["       latitude  housing_median_age  total_rooms  median_income  \\\n","0         34.19                15.0       5612.0         1.4936   \n","1         34.40                19.0       7650.0         1.8200   \n","2         33.69                17.0        720.0         1.6509   \n","3         33.64                14.0       1501.0         3.1917   \n","4         33.57                20.0       1454.0         1.9250   \n","...         ...                 ...          ...            ...   \n","16995     40.58                52.0       2217.0         2.3571   \n","16996     40.69                36.0       2349.0         2.5179   \n","16997     41.84                17.0       2677.0         3.0313   \n","16998     41.80                19.0       2672.0         1.9797   \n","16999     40.54                52.0       1820.0         3.0147   \n","\n","       median_house_value  \n","0                 66900.0  \n","1                 80100.0  \n","2                 85700.0  \n","3                 73400.0  \n","4                 65500.0  \n","...                   ...  \n","16995            111400.0  \n","16996             79000.0  \n","16997            103600.0  \n","16998             85800.0  \n","16999             94600.0  \n","\n","[17000 rows x 5 columns]"],"text/html":["\n","  <div id=\"df-ae6a0e15-b064-4e57-ac0b-edf2c6d88336\">\n","    <div 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<td>80100.0</td>\n","    </tr>\n","    <tr>\n","      <th>2</th>\n","      <td>33.69</td>\n","      <td>17.0</td>\n","      <td>720.0</td>\n","      <td>1.6509</td>\n","      <td>85700.0</td>\n","    </tr>\n","    <tr>\n","      <th>3</th>\n","      <td>33.64</td>\n","      <td>14.0</td>\n","      <td>1501.0</td>\n","      <td>3.1917</td>\n","      <td>73400.0</td>\n","    </tr>\n","    <tr>\n","      <th>4</th>\n","      <td>33.57</td>\n","      <td>20.0</td>\n","      <td>1454.0</td>\n","      <td>1.9250</td>\n","      <td>65500.0</td>\n","    </tr>\n","    <tr>\n","      <th>...</th>\n","      <td>...</td>\n","      <td>...</td>\n","      <td>...</td>\n","      <td>...</td>\n","      <td>...</td>\n","    </tr>\n","    <tr>\n","      <th>16995</th>\n","      <td>40.58</td>\n","      <td>52.0</td>\n","      <td>2217.0</td>\n","      <td>2.3571</td>\n","      <td>111400.0</td>\n","    </tr>\n","    <tr>\n","      <th>16996</th>\n","      <td>40.69</td>\n","      <td>36.0</td>\n","      <td>2349.0</td>\n","      <td>2.5179</td>\n","      <td>79000.0</td>\n","    </tr>\n","    <tr>\n","      <th>16997</th>\n","      <td>41.84</td>\n","      <td>17.0</td>\n","      <td>2677.0</td>\n","      <td>3.0313</td>\n","      <td>103600.0</td>\n","    </tr>\n","    <tr>\n","      <th>16998</th>\n","      <td>41.80</td>\n","      <td>19.0</td>\n","      <td>2672.0</td>\n","      <td>1.9797</td>\n","      <td>85800.0</td>\n","    </tr>\n","    <tr>\n","      <th>16999</th>\n","      <td>40.54</td>\n","      <td>52.0</td>\n","      <td>1820.0</td>\n","      <td>3.0147</td>\n","      <td>94600.0</td>\n","    </tr>\n","  </tbody>\n","</table>\n","<p>17000 rows × 5 columns</p>\n","</div>\n","      <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-ae6a0e15-b064-4e57-ac0b-edf2c6d88336')\"\n","              title=\"Convert this dataframe to an interactive table.\"\n","              style=\"display:none;\">\n","        \n","  <svg xmlns=\"http://www.w3.org/2000/svg\" 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40.58                52.0       2217.0         2.3571\n","16996     40.69                36.0       2349.0         2.5179\n","16997     41.84                17.0       2677.0         3.0313\n","16998     41.80                19.0       2672.0         1.9797\n","16999     40.54                52.0       1820.0         3.0147\n","\n","[17000 rows x 4 columns]"],"text/html":["\n","  <div id=\"df-10ed374d-792f-4a2f-b534-23faea9230e4\">\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>latitude</th>\n","      <th>housing_median_age</th>\n","      <th>total_rooms</th>\n","      <th>median_income</th>\n","    </tr>\n","  </thead>\n","  <tbody>\n","    <tr>\n","      <th>0</th>\n","      <td>34.19</td>\n","      <td>15.0</td>\n","      <td>5612.0</td>\n","      <td>1.4936</td>\n","    </tr>\n","    <tr>\n","      <th>1</th>\n","      <td>34.40</td>\n","      <td>19.0</td>\n","      <td>7650.0</td>\n","      <td>1.8200</td>\n","    </tr>\n","    <tr>\n","      <th>2</th>\n","      <td>33.69</td>\n","      <td>17.0</td>\n","      <td>720.0</td>\n","      <td>1.6509</td>\n","    </tr>\n","    <tr>\n","      <th>3</th>\n","      <td>33.64</td>\n","      <td>14.0</td>\n","      <td>1501.0</td>\n","      <td>3.1917</td>\n","    </tr>\n","    <tr>\n","      <th>4</th>\n","      <td>33.57</td>\n","      <td>20.0</td>\n","      <td>1454.0</td>\n","      <td>1.9250</td>\n","    </tr>\n","    <tr>\n","      <th>...</th>\n","      <td>...</td>\n","      <td>...</td>\n","      <td>...</td>\n","      <td>...</td>\n","    </tr>\n","    <tr>\n","      <th>16995</th>\n","      <td>40.58</td>\n","      <td>52.0</td>\n","      <td>2217.0</td>\n","      <td>2.3571</td>\n","    </tr>\n","    <tr>\n","      <th>16996</th>\n","      <td>40.69</td>\n","      <td>36.0</td>\n","      <td>2349.0</td>\n","      <td>2.5179</td>\n","    </tr>\n","    <tr>\n","      <th>16997</th>\n","      <td>41.84</td>\n","      <td>17.0</td>\n","      <td>2677.0</td>\n","      <td>3.0313</td>\n","    </tr>\n","    <tr>\n","      <th>16998</th>\n","      <td>41.80</td>\n","      <td>19.0</td>\n","      <td>2672.0</td>\n","      <td>1.9797</td>\n","    </tr>\n","    <tr>\n","      <th>16999</th>\n","      <td>40.54</td>\n","      <td>52.0</td>\n","      <td>1820.0</td>\n","      <td>3.0147</td>\n","    </tr>\n","  </tbody>\n","</table>\n","<p>17000 rows × 4 columns</p>\n","</div>\n","      <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-10ed374d-792f-4a2f-b534-23faea9230e4')\"\n","              title=\"Convert this dataframe to an interactive table.\"\n","        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answer)**2)"],"metadata":{"id":"Oza_RciBeUdD"},"execution_count":null,"outputs":[]},{"cell_type":"code","source":["rmse = np.sqrt(mse)"],"metadata":{"id":"xcg1KHsaeUdD"},"execution_count":null,"outputs":[]},{"cell_type":"code","source":["rmse"],"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"gl6qWSJAeV5-","executionInfo":{"status":"ok","timestamp":1675400915830,"user_tz":-330,"elapsed":39,"user":{"displayName":"Mohit Manghwani","userId":"11157237833313497817"}},"outputId":"a989492d-b426-48a2-b09c-c6b937a35331"},"execution_count":null,"outputs":[{"output_type":"execute_result","data":{"text/plain":["79817.22603819976"]},"metadata":{},"execution_count":84}]},{"cell_type":"markdown","source":["1.data filtering (by selecting the input features those who has high correlation with target feature) \n","\n","---\n","\n","\n","2.data normalization  \n","types of normalization :  \n","min normalization(each column will be divided by min of data) ,  \n","max normalization(each column will be divided by min of data) ,  \n","standard normalization(mostly used)(converting the data into the z score)  \n","\n","---\n","\n","\n","3.data filtering (by selecting those input features which do not have high correlation with each other but have a high correlation with the target,reason ,because we will not be able to create a good vector space to be learnt in linear algebra) this can be solved by pca or feature selection\n","pca - uses dimension reduction by transforming data  \n","feature selection - uses direction inspection over the persorn correlation matrix\n"],"metadata":{"id":"OU5qVfJIfhwQ"}},{"cell_type":"code","source":["new_question = (new_question-new_question.mean())/new_question.std()\n","answer = (answer-answer.mean())/answer.std()"],"metadata":{"id":"hPq3B86Bn-l0"},"execution_count":null,"outputs":[]},{"cell_type":"code","source":["new_question"],"metadata":{"colab":{"base_uri":"https://localhost:8080/","height":424},"id":"JEyvjwSGocCM","executionInfo":{"status":"ok","timestamp":1675401043082,"user_tz":-330,"elapsed":5,"user":{"displayName":"Mohit Manghwani","userId":"11157237833313497817"}},"outputId":"3dc5d83a-f39d-4c45-9499-30e6c1272591"},"execution_count":null,"outputs":[{"output_type":"execute_result","data":{"text/plain":["       latitude  housing_median_age  total_rooms  median_income\n","0     -0.671500           -1.079639     1.361655      -1.252506\n","1     -0.573248           -0.761850     2.296540      -1.081451\n","2     -0.905436           -0.920744    -0.882436      -1.170071\n","3     -0.928830           -1.159087    -0.524171      -0.362590\n","4     -0.961581           -0.682402    -0.545731      -1.026424\n","...         ...                 ...          ...            ...\n","16995  2.318197            1.859916    -0.195722      -0.799975\n","16996  2.369663            0.588757    -0.135170      -0.715705\n","16997  2.907715           -0.920744     0.015292      -0.446650\n","16998  2.889000           -0.761850     0.012998      -0.997758\n","16999  2.299482            1.859916    -0.377837      -0.455349\n","\n","[17000 rows x 4 columns]"],"text/html":["\n","  <div id=\"df-c55e7780-86de-48a3-aae2-55776c28a925\">\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>latitude</th>\n","      <th>housing_median_age</th>\n","      <th>total_rooms</th>\n","      <th>median_income</th>\n","    </tr>\n","  </thead>\n","  <tbody>\n","    <tr>\n","      <th>0</th>\n","      <td>-0.671500</td>\n","      <td>-1.079639</td>\n","      <td>1.361655</td>\n","      <td>-1.252506</td>\n","    </tr>\n","    <tr>\n","      <th>1</th>\n","      <td>-0.573248</td>\n","      <td>-0.761850</td>\n","      <td>2.296540</td>\n","      <td>-1.081451</td>\n","    </tr>\n","    <tr>\n","      <th>2</th>\n","      <td>-0.905436</td>\n","      <td>-0.920744</td>\n","      <td>-0.882436</td>\n","      <td>-1.170071</td>\n","    </tr>\n","    <tr>\n","      <th>3</th>\n","      <td>-0.928830</td>\n","      <td>-1.159087</td>\n","      <td>-0.524171</td>\n","      <td>-0.362590</td>\n","    </tr>\n","    <tr>\n","      <th>4</th>\n","      <td>-0.961581</td>\n","      <td>-0.682402</td>\n","      <td>-0.545731</td>\n","      <td>-1.026424</td>\n","    </tr>\n","    <tr>\n","      <th>...</th>\n","      <td>...</td>\n","      <td>...</td>\n","      <td>...</td>\n","      <td>...</td>\n","    </tr>\n","    <tr>\n","      <th>16995</th>\n","      <td>2.318197</td>\n","      <td>1.859916</td>\n","      <td>-0.195722</td>\n","      <td>-0.799975</td>\n","    </tr>\n","    <tr>\n","      <th>16996</th>\n","      <td>2.369663</td>\n","      <td>0.588757</td>\n","      <td>-0.135170</td>\n","      <td>-0.715705</td>\n","    </tr>\n","    <tr>\n","      <th>16997</th>\n","      <td>2.907715</td>\n","      <td>-0.920744</td>\n","      <td>0.015292</td>\n","      <td>-0.446650</td>\n","    </tr>\n","    <tr>\n","      <th>16998</th>\n","      <td>2.889000</td>\n","      <td>-0.761850</td>\n","      <td>0.012998</td>\n","      <td>-0.997758</td>\n","    </tr>\n","    <tr>\n","      <th>16999</th>\n","      <td>2.299482</td>\n","      <td>1.859916</td>\n","      <td>-0.377837</td>\n","      <td>-0.455349</td>\n","    </tr>\n","  </tbody>\n","</table>\n","<p>17000 rows × 4 columns</p>\n","</div>\n","      <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-c55e7780-86de-48a3-aae2-55776c28a925')\"\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","       width=\"24px\">\n","    <path d=\"M0 0h24v24H0V0z\" fill=\"none\"/>\n","    <path d=\"M18.56 5.44l.94 2.06.94-2.06 2.06-.94-2.06-.94-.94-2.06-.94 2.06-2.06.94zm-11 1L8.5 8.5l.94-2.06 2.06-.94-2.06-.94L8.5 2.5l-.94 2.06-2.06.94zm10 10l.94 2.06.94-2.06 2.06-.94-2.06-.94-.94-2.06-.94 2.06-2.06.94z\"/><path d=\"M17.41 7.96l-1.37-1.37c-.4-.4-.92-.59-1.43-.59-.52 0-1.04.2-1.43.59L10.3 9.45l-7.72 7.72c-.78.78-.78 2.05 0 2.83L4 21.41c.39.39.9.59 1.41.59.51 0 1.02-.2 1.41-.59l7.78-7.78 2.81-2.81c.8-.78.8-2.07 0-2.86zM5.41 20L4 18.59l7.72-7.72 1.47 1.35L5.41 20z\"/>\n","  </svg>\n","      </button>\n","      \n","  <style>\n","    .colab-df-container {\n","      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Manghwani","userId":"11157237833313497817"}},"outputId":"0865a95a-a6b8-4e07-ae03-2343b59fe40d","id":"56ZEXhp_rJiB"},"execution_count":null,"outputs":[{"output_type":"execute_result","data":{"text/plain":["LinearRegression()"]},"metadata":{},"execution_count":94}]},{"cell_type":"code","source":["predictions = algo.predict(new_question)\n","predictions"],"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"executionInfo":{"status":"ok","timestamp":1675401045242,"user_tz":-330,"elapsed":2,"user":{"displayName":"Mohit Manghwani","userId":"11157237833313497817"}},"outputId":"4cb61c1d-b1f0-4528-8191-08d1e87988cd","id":"wMJsW2-arJiD"},"execution_count":null,"outputs":[{"output_type":"execute_result","data":{"text/plain":["array([-0.94864305, -0.70630767, -0.99037293, ..., -0.76794189,\n","       -1.11603772, -0.15205923])"]},"metadata":{},"execution_count":95}]},{"cell_type":"code","source":["mse = np.mean((predictions - answer)/answer**2)"],"metadata":{"id":"ywL2zinnrJiD"},"execution_count":null,"outputs":[]},{"cell_type":"code","source":["rmse = np.sqrt(mse)"],"metadata":{"id":"uRz8qcHXrJiD"},"execution_count":null,"outputs":[]},{"cell_type":"code","source":["rmse"],"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"executionInfo":{"status":"ok","timestamp":1675401049154,"user_tz":-330,"elapsed":2,"user":{"displayName":"Mohit Manghwani","userId":"11157237833313497817"}},"outputId":"a609dbbe-3e88-4203-912e-6df7f5dfb964","id":"x7JlWVG3rJiD"},"execution_count":null,"outputs":[{"output_type":"execute_result","data":{"text/plain":["0.6881758534042055"]},"metadata":{},"execution_count":98}]},{"cell_type":"code","source":["answer"],"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"lHwFmMxOrM9k","executionInfo":{"status":"ok","timestamp":1675401074340,"user_tz":-330,"elapsed":3,"user":{"displayName":"Mohit Manghwani","userId":"11157237833313497817"}},"outputId":"e04aad39-0dea-4ff4-ce97-32be745be9a3"},"execution_count":null,"outputs":[{"output_type":"execute_result","data":{"text/plain":["0       -1.210522\n","1       -1.096713\n","2       -1.048430\n","3       -1.154480\n","4       -1.222593\n","           ...   \n","16995   -0.826848\n","16996   -1.106197\n","16997   -0.894099\n","16998   -1.047568\n","16999   -0.971696\n","Name: median_house_value, Length: 17000, dtype: float64"]},"metadata":{},"execution_count":99}]},{"cell_type":"code","source":["from sklearn.metrics import r2_score"],"metadata":{"id":"sLZvAU1vr4_z"},"execution_count":null,"outputs":[]},{"cell_type":"code","source":["percentage = r2_score(y_true = answer,y_pred=predictions)"],"metadata":{"id":"OAegQ9Z86VAl"},"execution_count":null,"outputs":[]},{"cell_type":"code","source":["percentage"],"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"JVlMfOJd6rTV","executionInfo":{"status":"ok","timestamp":1675404978294,"user_tz":-330,"elapsed":2,"user":{"displayName":"Mohit Manghwani","userId":"11157237833313497817"}},"outputId":"5a936807-08cb-4138-c9b9-3571f82021ed"},"execution_count":null,"outputs":[{"output_type":"execute_result","data":{"text/plain":["0.5263861351522849"]},"metadata":{},"execution_count":106}]},{"cell_type":"markdown","source":["different dimension reduction algos':-  \n","pca - principal components analysis  \n","lda - linear discriminant analysis  \n","lsa - latent semantic analysis (used in chatgpt :)"],"metadata":{"id":"ettuIDfS8UVt"}},{"cell_type":"code","source":["question = (question-question.mean())/question.std()"],"metadata":{"id":"TpcLf0QX6szk"},"execution_count":null,"outputs":[]},{"cell_type":"code","source":["question"],"metadata":{"colab":{"base_uri":"https://localhost:8080/","height":424},"id":"CgeB1xQH9vc2","executionInfo":{"status":"ok","timestamp":1675405818928,"user_tz":-330,"elapsed":363,"user":{"displayName":"Mohit Manghwani","userId":"11157237833313497817"}},"outputId":"0c57e315-9d89-4573-c18a-acde09295252"},"execution_count":null,"outputs":[{"output_type":"execute_result","data":{"text/plain":["       longitude  latitude  housing_median_age  total_rooms  total_bedrooms  \\\n","0       2.619288 -0.671500           -1.079639     1.361655        1.764152   \n","1       2.539494 -0.573248           -0.761850     2.296540        3.230346   \n","2       2.494610 -0.905436           -0.920744    -0.882436       -0.866931   \n","3       2.489623 -0.928830           -1.159087    -0.524171       -0.480216   \n","4       2.489623 -0.961581           -0.682402    -0.545731       -0.506313   \n","...          ...       ...                 ...          ...             ...   \n","16995  -2.342894  2.318197            1.859916    -0.195722       -0.344985   \n","16996  -2.347881  2.369663            0.588757    -0.135170       -0.027072   \n","16997  -2.362842  2.907715           -0.920744     0.015292       -0.019955   \n","16998  -2.362842  2.889000           -0.761850     0.012998        0.029868   \n","16999  -2.387778  2.299482            1.859916    -0.377837       -0.567998   \n","\n","       population  households  median_income  \n","0       -0.361173   -0.075996      -1.252506  \n","1       -0.261858   -0.099401      -1.081451  \n","2       -0.955326   -0.999223      -1.170071  \n","3       -0.796769   -0.715753      -0.362590  \n","4       -0.701809   -0.622130      -1.026424  \n","...           ...         ...            ...  \n","16995   -0.455262   -0.343862      -0.799975  \n","16996   -0.205230   -0.094200      -0.715705  \n","16997   -0.161670   -0.117606      -0.446650  \n","16998   -0.114626   -0.060392      -0.997758  \n","16999   -0.543252   -0.601325      -0.455349  \n","\n","[17000 rows x 8 columns]"],"text/html":["\n","  <div id=\"df-bf50edf1-418c-4ec5-9dfe-f7ab1ba50da3\">\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>longitude</th>\n","      <th>latitude</th>\n","      <th>housing_median_age</th>\n","      <th>total_rooms</th>\n","      <th>total_bedrooms</th>\n","      <th>population</th>\n","      <th>households</th>\n","      <th>median_income</th>\n","    </tr>\n","  </thead>\n","  <tbody>\n","    <tr>\n","      <th>0</th>\n","      <td>2.619288</td>\n","      <td>-0.671500</td>\n","      <td>-1.079639</td>\n","      <td>1.361655</td>\n","      <td>1.764152</td>\n","      <td>-0.361173</td>\n","      <td>-0.075996</td>\n","      <td>-1.252506</td>\n","    </tr>\n","    <tr>\n","      <th>1</th>\n","      <td>2.539494</td>\n","      <td>-0.573248</td>\n","      <td>-0.761850</td>\n","      <td>2.296540</td>\n","      <td>3.230346</td>\n","      <td>-0.261858</td>\n","      <td>-0.099401</td>\n","      <td>-1.081451</td>\n","    </tr>\n","    <tr>\n","      <th>2</th>\n","      <td>2.494610</td>\n","      <td>-0.905436</td>\n","      <td>-0.920744</td>\n","      <td>-0.882436</td>\n","      <td>-0.866931</td>\n","      <td>-0.955326</td>\n","      <td>-0.999223</td>\n","      <td>-1.170071</td>\n","    </tr>\n","    <tr>\n","      <th>3</th>\n","      <td>2.489623</td>\n","      <td>-0.928830</td>\n","      <td>-1.159087</td>\n","      <td>-0.524171</td>\n","      <td>-0.480216</td>\n","      <td>-0.796769</td>\n","      <td>-0.715753</td>\n","      <td>-0.362590</td>\n","    </tr>\n","    <tr>\n","      <th>4</th>\n","      <td>2.489623</td>\n","      <td>-0.961581</td>\n","      <td>-0.682402</td>\n","      <td>-0.545731</td>\n","      <td>-0.506313</td>\n","      <td>-0.701809</td>\n","      <td>-0.622130</td>\n","      <td>-1.026424</td>\n","    </tr>\n","    <tr>\n","      <th>...</th>\n","      <td>...</td>\n","      <td>...</td>\n","      <td>...</td>\n","      <td>...</td>\n","      <td>...</td>\n","      <td>...</td>\n","      <td>...</td>\n","      <td>...</td>\n","    </tr>\n","    <tr>\n","      <th>16995</th>\n","      <td>-2.342894</td>\n","      <td>2.318197</td>\n","      <td>1.859916</td>\n","      <td>-0.195722</td>\n","      <td>-0.344985</td>\n","      <td>-0.455262</td>\n","      <td>-0.343862</td>\n","      <td>-0.799975</td>\n","    </tr>\n","    <tr>\n","      <th>16996</th>\n","      <td>-2.347881</td>\n","      <td>2.369663</td>\n","      <td>0.588757</td>\n","      <td>-0.135170</td>\n","      <td>-0.027072</td>\n","      <td>-0.205230</td>\n","      <td>-0.094200</td>\n","      <td>-0.715705</td>\n","    </tr>\n","    <tr>\n","      <th>16997</th>\n","      <td>-2.362842</td>\n","      <td>2.907715</td>\n","      <td>-0.920744</td>\n","      <td>0.015292</td>\n","      <td>-0.019955</td>\n","      <td>-0.161670</td>\n","      <td>-0.117606</td>\n","      <td>-0.446650</td>\n","    </tr>\n","    <tr>\n","      <th>16998</th>\n","      <td>-2.362842</td>\n","      <td>2.889000</td>\n","      <td>-0.761850</td>\n","      <td>0.012998</td>\n","      <td>0.029868</td>\n","      <td>-0.114626</td>\n","      <td>-0.060392</td>\n","      <td>-0.997758</td>\n","    </tr>\n","    <tr>\n","      <th>16999</th>\n","      <td>-2.387778</td>\n","      <td>2.299482</td>\n","      <td>1.859916</td>\n","      <td>-0.377837</td>\n","      <td>-0.567998</td>\n","      <td>-0.543252</td>\n","      <td>-0.601325</td>\n","      <td>-0.455349</td>\n","    </tr>\n","  </tbody>\n","</table>\n","<p>17000 rows × 8 columns</p>\n","</div>\n","      <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-bf50edf1-418c-4ec5-9dfe-f7ab1ba50da3')\"\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","       width=\"24px\">\n","    <path d=\"M0 0h24v24H0V0z\" fill=\"none\"/>\n","    <path d=\"M18.56 5.44l.94 2.06.94-2.06 2.06-.94-2.06-.94-.94-2.06-.94 2.06-2.06.94zm-11 1L8.5 8.5l.94-2.06 2.06-.94-2.06-.94L8.5 2.5l-.94 2.06-2.06.94zm10 10l.94 2.06.94-2.06 2.06-.94-2.06-.94-.94-2.06-.94 2.06-2.06.94z\"/><path d=\"M17.41 7.96l-1.37-1.37c-.4-.4-.92-.59-1.43-.59-.52 0-1.04.2-1.43.59L10.3 9.45l-7.72 7.72c-.78.78-.78 2.05 0 2.83L4 21.41c.39.39.9.59 1.41.59.51 0 1.02-.2 1.41-.59l7.78-7.78 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