house_price_pred_LR_direct.ipynb

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\\\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","\n","   population  households  median_income  median_house_value  \n","1      1129.0       463.0         1.8200             80100.0  \n","2       333.0       117.0         1.6509             85700.0  "],"text/html":["\n","  <div id=\"df-b2483851-5cf2-4de7-bcc4-b1f2a835f9e5\">\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","      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    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-ff9eed4a-4b76-4229-9cd2-80d8efd803be\">\n","    <div class=\"colab-df-container\">\n","      <div>\n","<style scoped>\n","    .dataframe tbody tr th:only-of-type {\n","        vertical-align: 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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","      <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","   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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":196}]},{"cell_type":"code","source":["from sklearn.linear_model import LinearRegression as lr"],"metadata":{"id":"fX_g7leeu3JW"},"execution_count":null,"outputs":[]},{"cell_type":"code","source":["x = df.iloc[:,0:8]\n","y = df.iloc[:,8]"],"metadata":{"id":"axsCue_hvzoG"},"execution_count":null,"outputs":[]},{"cell_type":"code","source":["y"],"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"AuqMyEns2wD_","executionInfo":{"status":"ok","timestamp":1674802671617,"user_tz":-330,"elapsed":10,"user":{"displayName":"saksham Solanki","userId":"13629455334276929236"}},"outputId":"98409253-0676-4874-f9f3-a972f03e34be"},"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":199}]},{"cell_type":"code","source":["x"],"metadata":{"colab":{"base_uri":"https://localhost:8080/","height":424},"id":"SJtQ3Q6c3hXu","executionInfo":{"status":"ok","timestamp":1674802672022,"user_tz":-330,"elapsed":19,"user":{"displayName":"saksham Solanki","userId":"13629455334276929236"}},"outputId":"6b9d7e61-5a0d-4279-ef64-2d74eee443a0"},"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  \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-8a6e10ce-c8be-4ad8-ae91-4815d9440eb5\">\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>-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","    </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","    </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-8a6e10ce-c8be-4ad8-ae91-4815d9440eb5')\"\n","              title=\"Convert this dataframe to an 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LinearRegression()"],"metadata":{"id":"3oMe_l3h4G0v"},"execution_count":null,"outputs":[]},{"cell_type":"code","source":["x"],"metadata":{"colab":{"base_uri":"https://localhost:8080/","height":424},"id":"fQS_EpnZ5DnW","executionInfo":{"status":"ok","timestamp":1674802672023,"user_tz":-330,"elapsed":17,"user":{"displayName":"saksham Solanki","userId":"13629455334276929236"}},"outputId":"295be699-d8d6-498c-a9b6-2c274a809adb"},"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  \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-7829f75b-20b8-482d-bc61-ce9fedeaa11e\">\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>-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","    </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","    </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","    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np.array(test.iloc[:,8])"],"metadata":{"id":"IscedPCr9ZjX"},"execution_count":null,"outputs":[]},{"cell_type":"code","source":["sub = np.abs(np.subtract(pred,ans))"],"metadata":{"id":"RF3inwIE9_JX"},"execution_count":null,"outputs":[]},{"cell_type":"code","source":["prob = np.divide(sub,ans)\n","prob"],"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"8qY1GhfX-GsX","executionInfo":{"status":"ok","timestamp":1674802672024,"user_tz":-330,"elapsed":10,"user":{"displayName":"saksham Solanki","userId":"13629455334276929236"}},"outputId":"5d388aa8-3cbf-4eec-c2d1-5354de562db3"},"execution_count":null,"outputs":[{"output_type":"execute_result","data":{"text/plain":["array([0.02353441, 0.20519944, 0.00681959, ..., 0.42291268, 0.09923277,\n","       0.0864426 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-43221.30247953])"]},"metadata":{},"execution_count":211}]},{"cell_type":"code","source":[],"metadata":{"id":"eFWM8LkdBUIf"},"execution_count":null,"outputs":[]}]}