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houseprice detection
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houseprice detection
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{"nbformat":4,"nbformat_minor":0,"metadata":{"colab":{"name":"houseprice detection","provenance":[],"authorship_tag":"ABX9TyPbs2FNt9nRxI83Luf6ExP2"},"kernelspec":{"name":"python3","display_name":"Python 3"},"language_info":{"name":"python"}},"cells":[{"cell_type":"code","metadata":{"id":"RVzsYAt6zoDv","executionInfo":{"status":"ok","timestamp":1638196325063,"user_tz":-330,"elapsed":1459,"user":{"displayName":"SRI KALYAN","photoUrl":"https://lh3.googleusercontent.com/a/default-user=s64","userId":"11834340172146188146"}}},"source":["import numpy as np\n","import pandas as pd\n","import matplotlib.pyplot as plt\n","import seaborn as sns\n","import sklearn.datasets\n","from sklearn.model_selection import train_test_split\n","from xgboost import XGBRegressor\n","from sklearn import metrics\n"],"execution_count":2,"outputs":[]},{"cell_type":"code","metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"rXSDs1Q8JyAo","executionInfo":{"status":"ok","timestamp":1638196327355,"user_tz":-330,"elapsed":470,"user":{"displayName":"SRI KALYAN","photoUrl":"https://lh3.googleusercontent.com/a/default-user=s64","userId":"11834340172146188146"}},"outputId":"f8de4970-cbe9-4ea2-f87c-31a0ebf90ca1"},"source":["hp_dataset=sklearn.datasets.load_boston()"],"execution_count":3,"outputs":[{"output_type":"stream","name":"stderr","text":["/usr/local/lib/python3.7/dist-packages/sklearn/utils/deprecation.py:87: FutureWarning: Function load_boston is deprecated; `load_boston` is deprecated in 1.0 and will be removed in 1.2.\n","\n"," The Boston housing prices dataset has an ethical problem. You can refer to\n"," the documentation of this function for further details.\n","\n"," The scikit-learn maintainers therefore strongly discourage the use of this\n"," dataset unless the purpose of the code is to study and educate about\n"," ethical issues in data science and machine learning.\n","\n"," In this special case, you can fetch the dataset from the original\n"," source::\n","\n"," import pandas as pd\n"," import numpy as np\n","\n","\n"," data_url = \"http://lib.stat.cmu.edu/datasets/boston\"\n"," raw_df = pd.read_csv(data_url, sep=\"\\s+\", skiprows=22, header=None)\n"," data = np.hstack([raw_df.values[::2, :], raw_df.values[1::2, :2]])\n"," target = raw_df.values[1::2, 2]\n","\n"," Alternative datasets include the California housing dataset (i.e.\n"," :func:`~sklearn.datasets.fetch_california_housing`) and the Ames housing\n"," dataset. You can load the datasets as follows::\n","\n"," from sklearn.datasets import fetch_california_housing\n"," housing = fetch_california_housing()\n","\n"," for the California housing dataset and::\n","\n"," from sklearn.datasets import fetch_openml\n"," housing = fetch_openml(name=\"house_prices\", as_frame=True)\n","\n"," for the Ames housing dataset.\n"," \n"," warnings.warn(msg, category=FutureWarning)\n"]}]},{"cell_type":"code","metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"5bsl2LB0Jx94","executionInfo":{"status":"ok","timestamp":1638196330084,"user_tz":-330,"elapsed":420,"user":{"displayName":"SRI KALYAN","photoUrl":"https://lh3.googleusercontent.com/a/default-user=s64","userId":"11834340172146188146"}},"outputId":"b386c09f-5e65-4bdf-f1ce-85d3f5e2b6be"},"source":["print(hp_dataset)"],"execution_count":4,"outputs":[{"output_type":"stream","name":"stdout","text":["{'data': array([[6.3200e-03, 1.8000e+01, 2.3100e+00, ..., 1.5300e+01, 3.9690e+02,\n"," 4.9800e+00],\n"," [2.7310e-02, 0.0000e+00, 7.0700e+00, ..., 1.7800e+01, 3.9690e+02,\n"," 9.1400e+00],\n"," [2.7290e-02, 0.0000e+00, 7.0700e+00, ..., 1.7800e+01, 3.9283e+02,\n"," 4.0300e+00],\n"," ...,\n"," [6.0760e-02, 0.0000e+00, 1.1930e+01, ..., 2.1000e+01, 3.9690e+02,\n"," 5.6400e+00],\n"," [1.0959e-01, 0.0000e+00, 1.1930e+01, ..., 2.1000e+01, 3.9345e+02,\n"," 6.4800e+00],\n"," [4.7410e-02, 0.0000e+00, 1.1930e+01, ..., 2.1000e+01, 3.9690e+02,\n"," 7.8800e+00]]), 'target': array([24. , 21.6, 34.7, 33.4, 36.2, 28.7, 22.9, 27.1, 16.5, 18.9, 15. ,\n"," 18.9, 21.7, 20.4, 18.2, 19.9, 23.1, 17.5, 20.2, 18.2, 13.6, 19.6,\n"," 15.2, 14.5, 15.6, 13.9, 16.6, 14.8, 18.4, 21. , 12.7, 14.5, 13.2,\n"," 13.1, 13.5, 18.9, 20. , 21. , 24.7, 30.8, 34.9, 26.6, 25.3, 24.7,\n"," 21.2, 19.3, 20. , 16.6, 14.4, 19.4, 19.7, 20.5, 25. , 23.4, 18.9,\n"," 35.4, 24.7, 31.6, 23.3, 19.6, 18.7, 16. , 22.2, 25. , 33. , 23.5,\n"," 19.4, 22. , 17.4, 20.9, 24.2, 21.7, 22.8, 23.4, 24.1, 21.4, 20. ,\n"," 20.8, 21.2, 20.3, 28. , 23.9, 24.8, 22.9, 23.9, 26.6, 22.5, 22.2,\n"," 23.6, 28.7, 22.6, 22. , 22.9, 25. , 20.6, 28.4, 21.4, 38.7, 43.8,\n"," 33.2, 27.5, 26.5, 18.6, 19.3, 20.1, 19.5, 19.5, 20.4, 19.8, 19.4,\n"," 21.7, 22.8, 18.8, 18.7, 18.5, 18.3, 21.2, 19.2, 20.4, 19.3, 22. ,\n"," 20.3, 20.5, 17.3, 18.8, 21.4, 15.7, 16.2, 18. , 14.3, 19.2, 19.6,\n"," 23. , 18.4, 15.6, 18.1, 17.4, 17.1, 13.3, 17.8, 14. , 14.4, 13.4,\n"," 15.6, 11.8, 13.8, 15.6, 14.6, 17.8, 15.4, 21.5, 19.6, 15.3, 19.4,\n"," 17. , 15.6, 13.1, 41.3, 24.3, 23.3, 27. , 50. , 50. , 50. , 22.7,\n"," 25. , 50. , 23.8, 23.8, 22.3, 17.4, 19.1, 23.1, 23.6, 22.6, 29.4,\n"," 23.2, 24.6, 29.9, 37.2, 39.8, 36.2, 37.9, 32.5, 26.4, 29.6, 50. ,\n"," 32. , 29.8, 34.9, 37. , 30.5, 36.4, 31.1, 29.1, 50. , 33.3, 30.3,\n"," 34.6, 34.9, 32.9, 24.1, 42.3, 48.5, 50. , 22.6, 24.4, 22.5, 24.4,\n"," 20. , 21.7, 19.3, 22.4, 28.1, 23.7, 25. , 23.3, 28.7, 21.5, 23. ,\n"," 26.7, 21.7, 27.5, 30.1, 44.8, 50. , 37.6, 31.6, 46.7, 31.5, 24.3,\n"," 31.7, 41.7, 48.3, 29. , 24. , 25.1, 31.5, 23.7, 23.3, 22. , 20.1,\n"," 22.2, 23.7, 17.6, 18.5, 24.3, 20.5, 24.5, 26.2, 24.4, 24.8, 29.6,\n"," 42.8, 21.9, 20.9, 44. , 50. , 36. , 30.1, 33.8, 43.1, 48.8, 31. ,\n"," 36.5, 22.8, 30.7, 50. , 43.5, 20.7, 21.1, 25.2, 24.4, 35.2, 32.4,\n"," 32. , 33.2, 33.1, 29.1, 35.1, 45.4, 35.4, 46. , 50. , 32.2, 22. ,\n"," 20.1, 23.2, 22.3, 24.8, 28.5, 37.3, 27.9, 23.9, 21.7, 28.6, 27.1,\n"," 20.3, 22.5, 29. , 24.8, 22. , 26.4, 33.1, 36.1, 28.4, 33.4, 28.2,\n"," 22.8, 20.3, 16.1, 22.1, 19.4, 21.6, 23.8, 16.2, 17.8, 19.8, 23.1,\n"," 21. , 23.8, 23.1, 20.4, 18.5, 25. , 24.6, 23. , 22.2, 19.3, 22.6,\n"," 19.8, 17.1, 19.4, 22.2, 20.7, 21.1, 19.5, 18.5, 20.6, 19. , 18.7,\n"," 32.7, 16.5, 23.9, 31.2, 17.5, 17.2, 23.1, 24.5, 26.6, 22.9, 24.1,\n"," 18.6, 30.1, 18.2, 20.6, 17.8, 21.7, 22.7, 22.6, 25. , 19.9, 20.8,\n"," 16.8, 21.9, 27.5, 21.9, 23.1, 50. , 50. , 50. , 50. , 50. , 13.8,\n"," 13.8, 15. , 13.9, 13.3, 13.1, 10.2, 10.4, 10.9, 11.3, 12.3, 8.8,\n"," 7.2, 10.5, 7.4, 10.2, 11.5, 15.1, 23.2, 9.7, 13.8, 12.7, 13.1,\n"," 12.5, 8.5, 5. , 6.3, 5.6, 7.2, 12.1, 8.3, 8.5, 5. , 11.9,\n"," 27.9, 17.2, 27.5, 15. , 17.2, 17.9, 16.3, 7. , 7.2, 7.5, 10.4,\n"," 8.8, 8.4, 16.7, 14.2, 20.8, 13.4, 11.7, 8.3, 10.2, 10.9, 11. ,\n"," 9.5, 14.5, 14.1, 16.1, 14.3, 11.7, 13.4, 9.6, 8.7, 8.4, 12.8,\n"," 10.5, 17.1, 18.4, 15.4, 10.8, 11.8, 14.9, 12.6, 14.1, 13. , 13.4,\n"," 15.2, 16.1, 17.8, 14.9, 14.1, 12.7, 13.5, 14.9, 20. , 16.4, 17.7,\n"," 19.5, 20.2, 21.4, 19.9, 19. , 19.1, 19.1, 20.1, 19.9, 19.6, 23.2,\n"," 29.8, 13.8, 13.3, 16.7, 12. , 14.6, 21.4, 23. , 23.7, 25. , 21.8,\n"," 20.6, 21.2, 19.1, 20.6, 15.2, 7. , 8.1, 13.6, 20.1, 21.8, 24.5,\n"," 23.1, 19.7, 18.3, 21.2, 17.5, 16.8, 22.4, 20.6, 23.9, 22. , 11.9]), 'feature_names': array(['CRIM', 'ZN', 'INDUS', 'CHAS', 'NOX', 'RM', 'AGE', 'DIS', 'RAD',\n"," 'TAX', 'PTRATIO', 'B', 'LSTAT'], dtype='<U7'), 'DESCR': \".. _boston_dataset:\\n\\nBoston house prices dataset\\n---------------------------\\n\\n**Data Set Characteristics:** \\n\\n :Number of Instances: 506 \\n\\n :Number of Attributes: 13 numeric/categorical predictive. Median Value (attribute 14) is usually the target.\\n\\n :Attribute Information (in order):\\n - CRIM per capita crime rate by town\\n - ZN proportion of residential land zoned for lots over 25,000 sq.ft.\\n - INDUS proportion of non-retail business acres per town\\n - CHAS Charles River dummy variable (= 1 if tract bounds river; 0 otherwise)\\n - NOX nitric oxides concentration (parts per 10 million)\\n - RM average number of rooms per dwelling\\n - AGE proportion of owner-occupied units built prior to 1940\\n - DIS weighted distances to five Boston employment centres\\n - RAD index of accessibility to radial highways\\n - TAX full-value property-tax rate per $10,000\\n - PTRATIO pupil-teacher ratio by town\\n - B 1000(Bk - 0.63)^2 where Bk is the proportion of black people by town\\n - LSTAT % lower status of the population\\n - MEDV Median value of owner-occupied homes in $1000's\\n\\n :Missing Attribute Values: None\\n\\n :Creator: Harrison, D. and Rubinfeld, D.L.\\n\\nThis is a copy of UCI ML housing dataset.\\nhttps://archive.ics.uci.edu/ml/machine-learning-databases/housing/\\n\\n\\nThis dataset was taken from the StatLib library which is maintained at Carnegie Mellon University.\\n\\nThe Boston house-price data of Harrison, D. and Rubinfeld, D.L. 'Hedonic\\nprices and the demand for clean air', J. Environ. Economics & Management,\\nvol.5, 81-102, 1978. Used in Belsley, Kuh & Welsch, 'Regression diagnostics\\n...', Wiley, 1980. N.B. Various transformations are used in the table on\\npages 244-261 of the latter.\\n\\nThe Boston house-price data has been used in many machine learning papers that address regression\\nproblems. \\n \\n.. topic:: References\\n\\n - Belsley, Kuh & Welsch, 'Regression diagnostics: Identifying Influential Data and Sources of Collinearity', Wiley, 1980. 244-261.\\n - Quinlan,R. (1993). Combining Instance-Based and Model-Based Learning. In Proceedings on the Tenth International Conference of Machine Learning, 236-243, University of Massachusetts, Amherst. Morgan Kaufmann.\\n\", 'filename': 'boston_house_prices.csv', 'data_module': 'sklearn.datasets.data'}\n"]}]},{"cell_type":"code","metadata":{"id":"WeesVaqsJx7_","executionInfo":{"status":"ok","timestamp":1638196334856,"user_tz":-330,"elapsed":404,"user":{"displayName":"SRI KALYAN","photoUrl":"https://lh3.googleusercontent.com/a/default-user=s64","userId":"11834340172146188146"}}},"source":["hp_df=pd.DataFrame(hp_dataset.data,columns=hp_dataset.feature_names)"],"execution_count":5,"outputs":[]},{"cell_type":"code","metadata":{"colab":{"base_uri":"https://localhost:8080/","height":226},"id":"ReHy7wKiJx6Z","executionInfo":{"status":"ok","timestamp":1638196336706,"user_tz":-330,"elapsed":9,"user":{"displayName":"SRI KALYAN","photoUrl":"https://lh3.googleusercontent.com/a/default-user=s64","userId":"11834340172146188146"}},"outputId":"d4652898-1426-4623-c65b-96f8250bc601"},"source":["hp_df.head()"],"execution_count":6,"outputs":[{"output_type":"execute_result","data":{"text/html":["<div>\n","<style scoped>\n"," .dataframe tbody tr th:only-of-type {\n"," vertical-align: middle;\n"," }\n","\n"," .dataframe tbody tr th {\n"," vertical-align: top;\n"," }\n","\n"," .dataframe thead th {\n"," text-align: right;\n"," }\n","</style>\n","<table border=\"1\" class=\"dataframe\">\n"," <thead>\n"," <tr style=\"text-align: right;\">\n"," <th></th>\n"," <th>CRIM</th>\n"," <th>ZN</th>\n"," <th>INDUS</th>\n"," <th>CHAS</th>\n"," <th>NOX</th>\n"," <th>RM</th>\n"," <th>AGE</th>\n"," <th>DIS</th>\n"," <th>RAD</th>\n"," <th>TAX</th>\n"," <th>PTRATIO</th>\n"," <th>B</th>\n"," <th>LSTAT</th>\n"," </tr>\n"," </thead>\n"," <tbody>\n"," <tr>\n"," <th>0</th>\n"," <td>0.00632</td>\n"," <td>18.0</td>\n"," <td>2.31</td>\n"," <td>0.0</td>\n"," <td>0.538</td>\n"," <td>6.575</td>\n"," <td>65.2</td>\n"," <td>4.0900</td>\n"," <td>1.0</td>\n"," <td>296.0</td>\n"," <td>15.3</td>\n"," <td>396.90</td>\n"," <td>4.98</td>\n"," </tr>\n"," <tr>\n"," <th>1</th>\n"," <td>0.02731</td>\n"," <td>0.0</td>\n"," <td>7.07</td>\n"," <td>0.0</td>\n"," <td>0.469</td>\n"," <td>6.421</td>\n"," <td>78.9</td>\n"," <td>4.9671</td>\n"," <td>2.0</td>\n"," <td>242.0</td>\n"," <td>17.8</td>\n"," <td>396.90</td>\n"," <td>9.14</td>\n"," </tr>\n"," <tr>\n"," <th>2</th>\n"," <td>0.02729</td>\n"," <td>0.0</td>\n"," <td>7.07</td>\n"," <td>0.0</td>\n"," <td>0.469</td>\n"," <td>7.185</td>\n"," <td>61.1</td>\n"," <td>4.9671</td>\n"," <td>2.0</td>\n"," <td>242.0</td>\n"," <td>17.8</td>\n"," <td>392.83</td>\n"," <td>4.03</td>\n"," </tr>\n"," <tr>\n"," <th>3</th>\n"," <td>0.03237</td>\n"," <td>0.0</td>\n"," <td>2.18</td>\n"," <td>0.0</td>\n"," <td>0.458</td>\n"," <td>6.998</td>\n"," <td>45.8</td>\n"," <td>6.0622</td>\n"," <td>3.0</td>\n"," <td>222.0</td>\n"," <td>18.7</td>\n"," <td>394.63</td>\n"," <td>2.94</td>\n"," </tr>\n"," <tr>\n"," <th>4</th>\n"," <td>0.06905</td>\n"," <td>0.0</td>\n"," <td>2.18</td>\n"," <td>0.0</td>\n"," <td>0.458</td>\n"," <td>7.147</td>\n"," <td>54.2</td>\n"," <td>6.0622</td>\n"," <td>3.0</td>\n"," <td>222.0</td>\n"," <td>18.7</td>\n"," <td>396.90</td>\n"," <td>5.33</td>\n"," </tr>\n"," </tbody>\n","</table>\n","</div>"],"text/plain":[" CRIM ZN INDUS CHAS NOX ... RAD TAX PTRATIO B LSTAT\n","0 0.00632 18.0 2.31 0.0 0.538 ... 1.0 296.0 15.3 396.90 4.98\n","1 0.02731 0.0 7.07 0.0 0.469 ... 2.0 242.0 17.8 396.90 9.14\n","2 0.02729 0.0 7.07 0.0 0.469 ... 2.0 242.0 17.8 392.83 4.03\n","3 0.03237 0.0 2.18 0.0 0.458 ... 3.0 222.0 18.7 394.63 2.94\n","4 0.06905 0.0 2.18 0.0 0.458 ... 3.0 222.0 18.7 396.90 5.33\n","\n","[5 rows x 13 columns]"]},"metadata":{},"execution_count":6}]},{"cell_type":"code","metadata":{"id":"-s5qFkb0Jx4m","executionInfo":{"status":"ok","timestamp":1638196339686,"user_tz":-330,"elapsed":464,"user":{"displayName":"SRI KALYAN","photoUrl":"https://lh3.googleusercontent.com/a/default-user=s64","userId":"11834340172146188146"}}},"source":["hp_df['price']=hp_dataset.target"],"execution_count":7,"outputs":[]},{"cell_type":"code","metadata":{"colab":{"base_uri":"https://localhost:8080/","height":226},"id":"a8I_640IJx3P","executionInfo":{"status":"ok","timestamp":1638196342446,"user_tz":-330,"elapsed":8,"user":{"displayName":"SRI KALYAN","photoUrl":"https://lh3.googleusercontent.com/a/default-user=s64","userId":"11834340172146188146"}},"outputId":"6bb675e0-1708-4aa9-d225-68464b772128"},"source":["hp_df.head()"],"execution_count":8,"outputs":[{"output_type":"execute_result","data":{"text/html":["<div>\n","<style scoped>\n"," .dataframe tbody tr th:only-of-type {\n"," vertical-align: middle;\n"," }\n","\n"," .dataframe tbody tr th {\n"," vertical-align: top;\n"," }\n","\n"," .dataframe thead th {\n"," text-align: right;\n"," }\n","</style>\n","<table border=\"1\" class=\"dataframe\">\n"," <thead>\n"," <tr style=\"text-align: right;\">\n"," <th></th>\n"," <th>CRIM</th>\n"," <th>ZN</th>\n"," <th>INDUS</th>\n"," <th>CHAS</th>\n"," <th>NOX</th>\n"," <th>RM</th>\n"," <th>AGE</th>\n"," <th>DIS</th>\n"," <th>RAD</th>\n"," <th>TAX</th>\n"," <th>PTRATIO</th>\n"," <th>B</th>\n"," <th>LSTAT</th>\n"," <th>price</th>\n"," </tr>\n"," </thead>\n"," <tbody>\n"," <tr>\n"," <th>0</th>\n"," <td>0.00632</td>\n"," <td>18.0</td>\n"," <td>2.31</td>\n"," <td>0.0</td>\n"," <td>0.538</td>\n"," <td>6.575</td>\n"," <td>65.2</td>\n"," <td>4.0900</td>\n"," <td>1.0</td>\n"," <td>296.0</td>\n"," <td>15.3</td>\n"," <td>396.90</td>\n"," <td>4.98</td>\n"," <td>24.0</td>\n"," </tr>\n"," <tr>\n"," <th>1</th>\n"," <td>0.02731</td>\n"," <td>0.0</td>\n"," <td>7.07</td>\n"," <td>0.0</td>\n"," <td>0.469</td>\n"," <td>6.421</td>\n"," <td>78.9</td>\n"," <td>4.9671</td>\n"," <td>2.0</td>\n"," <td>242.0</td>\n"," <td>17.8</td>\n"," <td>396.90</td>\n"," <td>9.14</td>\n"," <td>21.6</td>\n"," </tr>\n"," <tr>\n"," <th>2</th>\n"," <td>0.02729</td>\n"," <td>0.0</td>\n"," <td>7.07</td>\n"," <td>0.0</td>\n"," <td>0.469</td>\n"," <td>7.185</td>\n"," <td>61.1</td>\n"," <td>4.9671</td>\n"," <td>2.0</td>\n"," <td>242.0</td>\n"," <td>17.8</td>\n"," <td>392.83</td>\n"," <td>4.03</td>\n"," <td>34.7</td>\n"," </tr>\n"," <tr>\n"," <th>3</th>\n"," <td>0.03237</td>\n"," <td>0.0</td>\n"," <td>2.18</td>\n"," <td>0.0</td>\n"," <td>0.458</td>\n"," <td>6.998</td>\n"," <td>45.8</td>\n"," <td>6.0622</td>\n"," <td>3.0</td>\n"," <td>222.0</td>\n"," <td>18.7</td>\n"," <td>394.63</td>\n"," <td>2.94</td>\n"," <td>33.4</td>\n"," </tr>\n"," <tr>\n"," <th>4</th>\n"," <td>0.06905</td>\n"," <td>0.0</td>\n"," <td>2.18</td>\n"," <td>0.0</td>\n"," <td>0.458</td>\n"," <td>7.147</td>\n"," <td>54.2</td>\n"," <td>6.0622</td>\n"," <td>3.0</td>\n"," <td>222.0</td>\n"," <td>18.7</td>\n"," <td>396.90</td>\n"," <td>5.33</td>\n"," <td>36.2</td>\n"," </tr>\n"," </tbody>\n","</table>\n","</div>"],"text/plain":[" CRIM ZN INDUS CHAS NOX ... TAX PTRATIO B LSTAT price\n","0 0.00632 18.0 2.31 0.0 0.538 ... 296.0 15.3 396.90 4.98 24.0\n","1 0.02731 0.0 7.07 0.0 0.469 ... 242.0 17.8 396.90 9.14 21.6\n","2 0.02729 0.0 7.07 0.0 0.469 ... 242.0 17.8 392.83 4.03 34.7\n","3 0.03237 0.0 2.18 0.0 0.458 ... 222.0 18.7 394.63 2.94 33.4\n","4 0.06905 0.0 2.18 0.0 0.458 ... 222.0 18.7 396.90 5.33 36.2\n","\n","[5 rows x 14 columns]"]},"metadata":{},"execution_count":8}]},{"cell_type":"code","metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"Pei8xz1KJx1b","executionInfo":{"status":"ok","timestamp":1638196345252,"user_tz":-330,"elapsed":11,"user":{"displayName":"SRI KALYAN","photoUrl":"https://lh3.googleusercontent.com/a/default-user=s64","userId":"11834340172146188146"}},"outputId":"4cf35a09-ff08-4d3e-bee0-8a430efdfc9b"},"source":["hp_df.shape"],"execution_count":9,"outputs":[{"output_type":"execute_result","data":{"text/plain":["(506, 14)"]},"metadata":{},"execution_count":9}]},{"cell_type":"code","metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"q3znmr4ZJxx7","executionInfo":{"status":"ok","timestamp":1638196347859,"user_tz":-330,"elapsed":18,"user":{"displayName":"SRI KALYAN","photoUrl":"https://lh3.googleusercontent.com/a/default-user=s64","userId":"11834340172146188146"}},"outputId":"bcd7b12e-b613-49ad-a3e7-6aa732f9e99f"},"source":["hp_df.isnull().sum()"],"execution_count":10,"outputs":[{"output_type":"execute_result","data":{"text/plain":["CRIM 0\n","ZN 0\n","INDUS 0\n","CHAS 0\n","NOX 0\n","RM 0\n","AGE 0\n","DIS 0\n","RAD 0\n","TAX 0\n","PTRATIO 0\n","B 0\n","LSTAT 0\n","price 0\n","dtype: int64"]},"metadata":{},"execution_count":10}]},{"cell_type":"code","metadata":{"colab":{"base_uri":"https://localhost:8080/","height":320},"id":"7VHl2PtPJxvh","executionInfo":{"status":"ok","timestamp":1638196351185,"user_tz":-330,"elapsed":757,"user":{"displayName":"SRI KALYAN","photoUrl":"https://lh3.googleusercontent.com/a/default-user=s64","userId":"11834340172146188146"}},"outputId":"be97d794-9bc7-471c-9938-93872d42c878"},"source":["hp_df.describe()"],"execution_count":11,"outputs":[{"output_type":"execute_result","data":{"text/html":["<div>\n","<style scoped>\n"," .dataframe tbody tr th:only-of-type {\n"," vertical-align: middle;\n"," }\n","\n"," .dataframe tbody tr th {\n"," vertical-align: top;\n"," }\n","\n"," .dataframe thead th {\n"," text-align: right;\n"," }\n","</style>\n","<table border=\"1\" class=\"dataframe\">\n"," <thead>\n"," <tr style=\"text-align: right;\">\n"," <th></th>\n"," <th>CRIM</th>\n"," <th>ZN</th>\n"," <th>INDUS</th>\n"," <th>CHAS</th>\n"," <th>NOX</th>\n"," <th>RM</th>\n"," <th>AGE</th>\n"," <th>DIS</th>\n"," <th>RAD</th>\n"," <th>TAX</th>\n"," <th>PTRATIO</th>\n"," <th>B</th>\n"," <th>LSTAT</th>\n"," <th>price</th>\n"," </tr>\n"," </thead>\n"," <tbody>\n"," <tr>\n"," <th>count</th>\n"," <td>506.000000</td>\n"," <td>506.000000</td>\n"," <td>506.000000</td>\n"," <td>506.000000</td>\n"," <td>506.000000</td>\n"," <td>506.000000</td>\n"," <td>506.000000</td>\n"," <td>506.000000</td>\n"," <td>506.000000</td>\n"," <td>506.000000</td>\n"," <td>506.000000</td>\n"," <td>506.000000</td>\n"," <td>506.000000</td>\n"," <td>506.000000</td>\n"," </tr>\n"," <tr>\n"," <th>mean</th>\n"," <td>3.613524</td>\n"," <td>11.363636</td>\n"," <td>11.136779</td>\n"," <td>0.069170</td>\n"," <td>0.554695</td>\n"," <td>6.284634</td>\n"," <td>68.574901</td>\n"," <td>3.795043</td>\n"," <td>9.549407</td>\n"," <td>408.237154</td>\n"," <td>18.455534</td>\n"," <td>356.674032</td>\n"," <td>12.653063</td>\n"," <td>22.532806</td>\n"," </tr>\n"," <tr>\n"," <th>std</th>\n"," <td>8.601545</td>\n"," <td>23.322453</td>\n"," <td>6.860353</td>\n"," <td>0.253994</td>\n"," <td>0.115878</td>\n"," <td>0.702617</td>\n"," <td>28.148861</td>\n"," <td>2.105710</td>\n"," <td>8.707259</td>\n"," <td>168.537116</td>\n"," <td>2.164946</td>\n"," <td>91.294864</td>\n"," <td>7.141062</td>\n"," <td>9.197104</td>\n"," </tr>\n"," <tr>\n"," <th>min</th>\n"," <td>0.006320</td>\n"," <td>0.000000</td>\n"," <td>0.460000</td>\n"," <td>0.000000</td>\n"," <td>0.385000</td>\n"," <td>3.561000</td>\n"," <td>2.900000</td>\n"," <td>1.129600</td>\n"," <td>1.000000</td>\n"," <td>187.000000</td>\n"," <td>12.600000</td>\n"," <td>0.320000</td>\n"," <td>1.730000</td>\n"," <td>5.000000</td>\n"," </tr>\n"," <tr>\n"," <th>25%</th>\n"," <td>0.082045</td>\n"," <td>0.000000</td>\n"," <td>5.190000</td>\n"," <td>0.000000</td>\n"," <td>0.449000</td>\n"," <td>5.885500</td>\n"," <td>45.025000</td>\n"," <td>2.100175</td>\n"," <td>4.000000</td>\n"," <td>279.000000</td>\n"," <td>17.400000</td>\n"," <td>375.377500</td>\n"," <td>6.950000</td>\n"," <td>17.025000</td>\n"," </tr>\n"," <tr>\n"," <th>50%</th>\n"," <td>0.256510</td>\n"," <td>0.000000</td>\n"," <td>9.690000</td>\n"," <td>0.000000</td>\n"," <td>0.538000</td>\n"," <td>6.208500</td>\n"," <td>77.500000</td>\n"," <td>3.207450</td>\n"," <td>5.000000</td>\n"," <td>330.000000</td>\n"," <td>19.050000</td>\n"," <td>391.440000</td>\n"," <td>11.360000</td>\n"," <td>21.200000</td>\n"," </tr>\n"," <tr>\n"," <th>75%</th>\n"," <td>3.677083</td>\n"," <td>12.500000</td>\n"," <td>18.100000</td>\n"," <td>0.000000</td>\n"," <td>0.624000</td>\n"," <td>6.623500</td>\n"," <td>94.075000</td>\n"," <td>5.188425</td>\n"," <td>24.000000</td>\n"," <td>666.000000</td>\n"," <td>20.200000</td>\n"," <td>396.225000</td>\n"," <td>16.955000</td>\n"," <td>25.000000</td>\n"," </tr>\n"," <tr>\n"," <th>max</th>\n"," <td>88.976200</td>\n"," <td>100.000000</td>\n"," <td>27.740000</td>\n"," <td>1.000000</td>\n"," <td>0.871000</td>\n"," <td>8.780000</td>\n"," <td>100.000000</td>\n"," <td>12.126500</td>\n"," <td>24.000000</td>\n"," <td>711.000000</td>\n"," <td>22.000000</td>\n"," <td>396.900000</td>\n"," <td>37.970000</td>\n"," <td>50.000000</td>\n"," </tr>\n"," </tbody>\n","</table>\n","</div>"],"text/plain":[" CRIM ZN INDUS ... B LSTAT price\n","count 506.000000 506.000000 506.000000 ... 506.000000 506.000000 506.000000\n","mean 3.613524 11.363636 11.136779 ... 356.674032 12.653063 22.532806\n","std 8.601545 23.322453 6.860353 ... 91.294864 7.141062 9.197104\n","min 0.006320 0.000000 0.460000 ... 0.320000 1.730000 5.000000\n","25% 0.082045 0.000000 5.190000 ... 375.377500 6.950000 17.025000\n","50% 0.256510 0.000000 9.690000 ... 391.440000 11.360000 21.200000\n","75% 3.677083 12.500000 18.100000 ... 396.225000 16.955000 25.000000\n","max 88.976200 100.000000 27.740000 ... 396.900000 37.970000 50.000000\n","\n","[8 rows x 14 columns]"]},"metadata":{},"execution_count":11}]},{"cell_type":"code","metadata":{"id":"qYfbQqHwJxtE","executionInfo":{"status":"ok","timestamp":1638196354402,"user_tz":-330,"elapsed":465,"user":{"displayName":"SRI KALYAN","photoUrl":"https://lh3.googleusercontent.com/a/default-user=s64","userId":"11834340172146188146"}}},"source":[" #coreletion\n","\n","correlation=hp_df.corr()"],"execution_count":12,"outputs":[]},{"cell_type":"code","metadata":{"colab":{"base_uri":"https://localhost:8080/","height":596},"id":"MMYI2EUYJxrK","executionInfo":{"status":"ok","timestamp":1638196702292,"user_tz":-330,"elapsed":570,"user":{"displayName":"SRI KALYAN","photoUrl":"https://lh3.googleusercontent.com/a/default-user=s64","userId":"11834340172146188146"}},"outputId":"038c85a7-d09b-4c17-a1d7-1c3dc8455ae8"},"source":["plt.figure(figsize=(10,10))\n","sns.heatmap(correlation, cbar=True, square=True, fmt='.1f', annot_kws={'size':8}, cmap='Blues')"],"execution_count":17,"outputs":[{"output_type":"execute_result","data":{"text/plain":["<matplotlib.axes._subplots.AxesSubplot at 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\n","text/plain":["<Figure size 720x720 with 2 Axes>"]},"metadata":{"needs_background":"light"}}]},{"cell_type":"code","metadata":{"id":"gzOc01W-JxnU","executionInfo":{"status":"ok","timestamp":1638197357032,"user_tz":-330,"elapsed":422,"user":{"displayName":"SRI KALYAN","photoUrl":"https://lh3.googleusercontent.com/a/default-user=s64","userId":"11834340172146188146"}}},"source":["x=hp_df.drop(['price'],axis=1)\n","y=hp_df['price']"],"execution_count":21,"outputs":[]},{"cell_type":"code","metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"1qM_VUSZJxlj","executionInfo":{"status":"ok","timestamp":1638197370428,"user_tz":-330,"elapsed":718,"user":{"displayName":"SRI KALYAN","photoUrl":"https://lh3.googleusercontent.com/a/default-user=s64","userId":"11834340172146188146"}},"outputId":"422dc1b8-302b-4c80-cae9-5a674a19a698"},"source":["print(x)\n","print(y)"],"execution_count":22,"outputs":[{"output_type":"stream","name":"stdout","text":[" CRIM ZN INDUS CHAS NOX ... RAD TAX PTRATIO B LSTAT\n","0 0.00632 18.0 2.31 0.0 0.538 ... 1.0 296.0 15.3 396.90 4.98\n","1 0.02731 0.0 7.07 0.0 0.469 ... 2.0 242.0 17.8 396.90 9.14\n","2 0.02729 0.0 7.07 0.0 0.469 ... 2.0 242.0 17.8 392.83 4.03\n","3 0.03237 0.0 2.18 0.0 0.458 ... 3.0 222.0 18.7 394.63 2.94\n","4 0.06905 0.0 2.18 0.0 0.458 ... 3.0 222.0 18.7 396.90 5.33\n",".. ... ... ... ... ... ... ... ... ... ... ...\n","501 0.06263 0.0 11.93 0.0 0.573 ... 1.0 273.0 21.0 391.99 9.67\n","502 0.04527 0.0 11.93 0.0 0.573 ... 1.0 273.0 21.0 396.90 9.08\n","503 0.06076 0.0 11.93 0.0 0.573 ... 1.0 273.0 21.0 396.90 5.64\n","504 0.10959 0.0 11.93 0.0 0.573 ... 1.0 273.0 21.0 393.45 6.48\n","505 0.04741 0.0 11.93 0.0 0.573 ... 1.0 273.0 21.0 396.90 7.88\n","\n","[506 rows x 13 columns]\n","0 24.0\n","1 21.6\n","2 34.7\n","3 33.4\n","4 36.2\n"," ... \n","501 22.4\n","502 20.6\n","503 23.9\n","504 22.0\n","505 11.9\n","Name: price, Length: 506, dtype: float64\n"]}]},{"cell_type":"code","metadata":{"id":"H1BRxbA2Jxjc","executionInfo":{"status":"ok","timestamp":1638198190469,"user_tz":-330,"elapsed":435,"user":{"displayName":"SRI KALYAN","photoUrl":"https://lh3.googleusercontent.com/a/default-user=s64","userId":"11834340172146188146"}}},"source":["x_train,x_test,y_train,y_test=train_test_split(x,y,test_size=0.2,random_state=2)"],"execution_count":34,"outputs":[]},{"cell_type":"code","metadata":{"id":"ZrFFchFhJxhb","executionInfo":{"status":"ok","timestamp":1638198192818,"user_tz":-330,"elapsed":3,"user":{"displayName":"SRI KALYAN","photoUrl":"https://lh3.googleusercontent.com/a/default-user=s64","userId":"11834340172146188146"}}},"source":["model=XGBRegressor()"],"execution_count":35,"outputs":[]},{"cell_type":"code","metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"nKPI5jstJxfP","executionInfo":{"status":"ok","timestamp":1638198401216,"user_tz":-330,"elapsed":405,"user":{"displayName":"SRI KALYAN","photoUrl":"https://lh3.googleusercontent.com/a/default-user=s64","userId":"11834340172146188146"}},"outputId":"6a170e97-f6b7-49eb-d696-658f57843131"},"source":["model.fit(x_train,y_train)"],"execution_count":41,"outputs":[{"output_type":"stream","name":"stdout","text":["[15:06:40] WARNING: /workspace/src/objective/regression_obj.cu:152: reg:linear is now deprecated in favor of reg:squarederror.\n"]},{"output_type":"execute_result","data":{"text/plain":["XGBRegressor()"]},"metadata":{},"execution_count":41}]},{"cell_type":"code","metadata":{"id":"d1Bl6h_SJxdF","executionInfo":{"status":"ok","timestamp":1638201418019,"user_tz":-330,"elapsed":399,"user":{"displayName":"SRI KALYAN","photoUrl":"https://lh3.googleusercontent.com/a/default-user=s64","userId":"11834340172146188146"}}},"source":["training_data_prediction=model.predict(x_train)"],"execution_count":51,"outputs":[]},{"cell_type":"code","metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"m2eMs-ZjJxbG","executionInfo":{"status":"ok","timestamp":1638201420491,"user_tz":-330,"elapsed":8,"user":{"displayName":"SRI KALYAN","photoUrl":"https://lh3.googleusercontent.com/a/default-user=s64","userId":"11834340172146188146"}},"outputId":"20c684cb-7fd9-4d95-e9ca-46a9ee238ccf"},"source":["print(training_data_prediction)"],"execution_count":52,"outputs":[{"output_type":"stream","name":"stdout","text":["[23.360205 22.462858 20.84804 33.77895 15.333282 13.616525\n"," 21.71274 15.175322 11.724756 21.836252 16.08508 7.52517\n"," 31.094206 48.56228 32.623158 20.546066 22.177324 20.500404\n"," 31.666502 20.551508 25.74269 8.247894 45.200817 22.069397\n"," 20.698004 20.100042 19.873472 26.242834 23.39618 31.927258\n"," 21.493471 9.280926 18.504272 21.87202 12.504413 10.578829\n"," 13.054951 23.541336 19.164755 15.888303 23.768887 28.454714\n"," 15.539753 18.049202 16.23671 14.08383 25.33273 17.575668\n"," 49.566467 16.990675 21.738977 32.935143 16.125738 22.45393\n"," 20.776966 20.042227 22.898897 38.124043 30.607079 32.607468\n"," 20.919416 47.348038 14.524615 8.126455 19.581661 9.030508\n"," 26.462107 17.69918 20.546162 46.312218 39.689137 34.387108\n"," 22.11083 34.568977 24.873934 50.078335 14.5669775 20.525211\n"," 20.62971 23.202105 49.514477 23.12061 24.795782 20.319666\n"," 43.869396 17.110266 32.165016 34.75202 7.313497 20.309446\n"," 18.038298 12.008462 24.216425 47.90671 37.94349 20.759708\n"," 40.182804 18.249052 15.611586 26.39461 21.0571 20.421682\n"," 18.377089 17.338768 21.223648 22.653662 17.560051 32.635715\n"," 16.683764 13.004857 18.488163 20.659714 16.501846 20.648884\n"," 48.62411 15.977999 15.97522 18.581459 14.893438 32.871964\n"," 14.236945 43.612328 33.881115 19.073408 15.747335 9.4903965\n"," 10.153891 14.812717 18.655546 8.596755 22.666656 10.941623\n"," 20.534616 49.324417 22.710459 19.99658 31.663935 21.78586\n"," 30.9277 30.507492 15.054665 15.854853 48.532074 21.108742\n"," 15.687305 12.403721 49.90245 31.557863 11.709707 20.22495\n"," 26.214525 32.90807 22.90362 9.542897 24.487959 24.46598\n"," 22.509142 14.704502 27.895067 33.619015 14.888735 19.147383\n"," 26.40218 32.77208 29.293688 23.638102 10.448805 22.518728\n"," 21.47825 35.32415 23.002241 20.470022 18.918747 10.328174\n"," 22.244467 17.69918 20.918488 11.913417 42.572548 46.803394\n"," 14.652036 20.633188 23.285368 15.295161 20.861048 23.587011\n"," 32.94382 21.090906 24.898489 18.465925 31.454802 14.421506\n"," 15.421497 21.890705 23.64799 17.40471 26.111868 24.977922\n"," 27.56308 22.964123 18.823803 28.856464 14.080684 19.785515\n"," 17.007908 42.90537 26.354216 21.719929 23.784258 18.4141\n"," 17.923422 20.337881 22.936398 25.297531 17.572325 14.486319\n"," 20.739832 21.733093 11.1917715 18.290442 20.70475 20.929468\n"," 18.990923 8.7798395 21.141748 21.021317 15.49217 24.455221\n"," 31.499088 22.668139 14.862843 19.69585 24.746317 22.913176\n"," 48.144817 19.950285 30.148172 49.98047 16.743952 16.218952\n"," 9.891141 20.452726 17.06055 14.73646 17.539606 19.555712\n"," 30.26191 27.037518 18.43813 20.100842 24.147627 10.21256\n"," 25.064299 48.283043 20.977459 23.265625 20.141813 11.87677\n"," 17.84212 15.1286955 14.9789295 23.502743 16.092314 21.276255\n"," 26.55347 16.940031 23.485325 14.927286 20.90435 19.254526\n"," 24.397417 27.566774 23.607512 17.905067 22.675825 25.12203\n"," 15.141896 18.460642 23.440636 16.4928 23.372946 30.389936\n"," 15.330368 24.69199 17.316717 14.531138 10.496169 24.805672\n"," 15.659789 38.916733 20.403166 42.113743 8.544421 22.536352\n"," 15.654481 15.709977 17.263374 23.888586 21.690222 46.16276\n"," 15.304819 31.137545 25.326769 18.969254 26.29209 11.722559\n"," 40.65201 20.52522 17.135836 24.829275 15.565665 23.360205\n"," 8.280649 24.018639 19.57025 20.865868 23.611485 22.455328\n"," 17.646477 17.687094 14.59732 25.61237 13.333718 22.577513\n"," 20.657572 14.8804865 16.539358 23.276703 24.873934 22.52675\n"," 23.107155 31.871576 19.262531 19.536154 28.251024 23.817226\n"," 12.874959 22.59372 12.234834 10.024989 20.419611 10.369816\n"," 45.84478 24.873934 12.357825 16.367088 14.355771 28.338346\n"," 18.669233 20.334248 10.546778 21.30952 21.00914 20.669264\n"," 23.91886 25.009733 26.945326 13.288843 18.277857 20.95568\n"," 18.233625 23.807056 13.400126 23.875198 33.050533 27.785492\n"," 25.296518 19.071947 20.950756 11.507434 22.855497 15.573306\n"," 22.33747 20.807749 22.41908 17.212593 12.645366 35.121113\n"," 18.852188 48.823723 22.462465 24.267456 21.375692 19.38756\n"," 8.561088 20.726429 23.400837 21.41578 17.63176 25.232733\n"," 21.164701 26.444288 14.49171 49.559753 30.693232 23.20531\n"," 22.950115 16.84211 30.982431 16.259336 23.613512 20.93225\n"," 20.178421 22.782583 ]\n"]}]},{"cell_type":"code","metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"yxMWeBfiJxY9","executionInfo":{"status":"ok","timestamp":1638198414479,"user_tz":-330,"elapsed":377,"user":{"displayName":"SRI KALYAN","photoUrl":"https://lh3.googleusercontent.com/a/default-user=s64","userId":"11834340172146188146"}},"outputId":"b89a1b04-467a-48dd-e9e2-c3a694b11ac5"},"source":["score_1=metrics.r2_score(y_train,training_data_prediction)\n","\n","#mean absolute error\n","\n","score_2=metrics.mean_absolute_error(y_train,training_data_prediction)\n","\n","print('r squared error:',score_1)\n","print('mean absolute error:',score_2)"],"execution_count":44,"outputs":[{"output_type":"stream","name":"stdout","text":["r squared error: 0.9733349094832763\n","mean absolute error: 1.145314053261634\n"]}]},{"cell_type":"code","metadata":{"colab":{"base_uri":"https://localhost:8080/","height":295},"id":"EELOraV-JxW7","executionInfo":{"status":"ok","timestamp":1638201365062,"user_tz":-330,"elapsed":491,"user":{"displayName":"SRI KALYAN","photoUrl":"https://lh3.googleusercontent.com/a/default-user=s64","userId":"11834340172146188146"}},"outputId":"53c018c5-402c-4f33-90f4-bd5dfb0d8141"},"source":["plt.scatter(y_train,training_data_prediction)\n","plt.xlabel('actual prices')\n","plt.ylabel('predicted prices')\n","\n","plt.title(\"actual price vs predicted price\")\n","plt.show()"],"execution_count":49,"outputs":[{"output_type":"display_data","data":{"image/png":"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\n","text/plain":["<Figure size 432x288 with 1 Axes>"]},"metadata":{"needs_background":"light"}}]},{"cell_type":"code","metadata":{"id":"vwdpAfXYJxUM","executionInfo":{"status":"ok","timestamp":1638201276682,"user_tz":-330,"elapsed":481,"user":{"displayName":"SRI KALYAN","photoUrl":"https://lh3.googleusercontent.com/a/default-user=s64","userId":"11834340172146188146"}}},"source":[" from sklearn.ensemble import RandomForestClassifier\n"," from sklearn.metrics import accuracy_score\n","\n"],"execution_count":46,"outputs":[]},{"cell_type":"code","metadata":{"id":"iOXnCoW_JxSF","executionInfo":{"status":"ok","timestamp":1638201315856,"user_tz":-330,"elapsed":525,"user":{"displayName":"SRI KALYAN","photoUrl":"https://lh3.googleusercontent.com/a/default-user=s64","userId":"11834340172146188146"}}},"source":["model_1=RandomForestClassifier()"],"execution_count":47,"outputs":[]},{"cell_type":"code","metadata":{"colab":{"base_uri":"https://localhost:8080/","height":328},"id":"nqofKlzQJxPs","executionInfo":{"status":"error","timestamp":1638201461077,"user_tz":-330,"elapsed":433,"user":{"displayName":"SRI KALYAN","photoUrl":"https://lh3.googleusercontent.com/a/default-user=s64","userId":"11834340172146188146"}},"outputId":"6bf2c2ec-f1c6-47be-bad3-fc1ce202f0d0"},"source":["model_1.fit(x_train, y_train)"],"execution_count":55,"outputs":[{"output_type":"error","ename":"ValueError","evalue":"ignored","traceback":["\u001b[0;31m---------------------------------------------------------------------------\u001b[0m","\u001b[0;31mValueError\u001b[0m Traceback (most recent call last)","\u001b[0;32m<ipython-input-55-9b51f72f7500>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m()\u001b[0m\n\u001b[0;32m----> 1\u001b[0;31m \u001b[0mmodel_1\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mfit\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mx_train\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0my_train\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m","\u001b[0;32m/usr/local/lib/python3.7/dist-packages/sklearn/ensemble/_forest.py\u001b[0m in \u001b[0;36mfit\u001b[0;34m(self, X, y, sample_weight)\u001b[0m\n\u001b[1;32m 365\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mn_outputs_\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0my\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mshape\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;36m1\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 366\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 367\u001b[0;31m \u001b[0my\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mexpanded_class_weight\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_validate_y_class_weight\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0my\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 368\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 369\u001b[0m \u001b[0;32mif\u001b[0m \u001b[0mgetattr\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0my\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m\"dtype\"\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;32mNone\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;34m!=\u001b[0m \u001b[0mDOUBLE\u001b[0m \u001b[0;32mor\u001b[0m \u001b[0;32mnot\u001b[0m \u001b[0my\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mflags\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mcontiguous\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n","\u001b[0;32m/usr/local/lib/python3.7/dist-packages/sklearn/ensemble/_forest.py\u001b[0m in \u001b[0;36m_validate_y_class_weight\u001b[0;34m(self, y)\u001b[0m\n\u001b[1;32m 722\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 723\u001b[0m \u001b[0;32mdef\u001b[0m \u001b[0m_validate_y_class_weight\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0my\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 724\u001b[0;31m \u001b[0mcheck_classification_targets\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0my\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 725\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 726\u001b[0m \u001b[0my\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mnp\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mcopy\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0my\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n","\u001b[0;32m/usr/local/lib/python3.7/dist-packages/sklearn/utils/multiclass.py\u001b[0m in \u001b[0;36mcheck_classification_targets\u001b[0;34m(y)\u001b[0m\n\u001b[1;32m 196\u001b[0m \u001b[0;34m\"multilabel-sequences\"\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 197\u001b[0m ]:\n\u001b[0;32m--> 198\u001b[0;31m \u001b[0;32mraise\u001b[0m \u001b[0mValueError\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m\"Unknown label type: %r\"\u001b[0m \u001b[0;34m%\u001b[0m \u001b[0my_type\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 199\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 200\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n","\u001b[0;31mValueError\u001b[0m: Unknown label type: 'continuous'"]}]},{"cell_type":"code","metadata":{"colab":{"base_uri":"https://localhost:8080/","height":345},"id":"j_qZTs3hJxNB","executionInfo":{"status":"error","timestamp":1638201506461,"user_tz":-330,"elapsed":609,"user":{"displayName":"SRI KALYAN","photoUrl":"https://lh3.googleusercontent.com/a/default-user=s64","userId":"11834340172146188146"}},"outputId":"4672a4d9-c2ab-4b05-fadd-1ef302803b2c"},"source":["#accuracy\n","x_test_prediction=model_1.predict(x_test)\n","test_data_accuracy=accuracy_score(x_test_prediction,y_test)"],"execution_count":56,"outputs":[{"output_type":"error","ename":"AttributeError","evalue":"ignored","traceback":["\u001b[0;31m---------------------------------------------------------------------------\u001b[0m","\u001b[0;31mAttributeError\u001b[0m Traceback (most recent call last)","\u001b[0;32m<ipython-input-56-a4f6ed55e704>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m()\u001b[0m\n\u001b[1;32m 1\u001b[0m \u001b[0;31m#accuracy\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m----> 2\u001b[0;31m \u001b[0mx_test_prediction\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mmodel_1\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mpredict\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mx_test\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m","\u001b[0;32m/usr/local/lib/python3.7/dist-packages/sklearn/ensemble/_forest.py\u001b[0m in \u001b[0;36mpredict\u001b[0;34m(self, X)\u001b[0m\n\u001b[1;32m 796\u001b[0m \u001b[0mThe\u001b[0m \u001b[0mpredicted\u001b[0m \u001b[0mclasses\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 797\u001b[0m \"\"\"\n\u001b[0;32m--> 798\u001b[0;31m \u001b[0mproba\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mpredict_proba\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mX\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 799\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 800\u001b[0m \u001b[0;32mif\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mn_outputs_\u001b[0m \u001b[0;34m==\u001b[0m \u001b[0;36m1\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n","\u001b[0;32m/usr/local/lib/python3.7/dist-packages/sklearn/ensemble/_forest.py\u001b[0m in \u001b[0;36mpredict_proba\u001b[0;34m(self, X)\u001b[0m\n\u001b[1;32m 846\u001b[0m all_proba = [\n\u001b[1;32m 847\u001b[0m \u001b[0mnp\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mzeros\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mX\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mshape\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;36m0\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mj\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mdtype\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mnp\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mfloat64\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 848\u001b[0;31m \u001b[0;32mfor\u001b[0m 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'n_classes_'"]}]},{"cell_type":"code","metadata":{"id":"t0QyK3MyJxKx"},"source":[""],"execution_count":null,"outputs":[]},{"cell_type":"code","metadata":{"id":"_iJmta7mJxIn"},"source":[""],"execution_count":null,"outputs":[]},{"cell_type":"code","metadata":{"id":"-PCGi4_ZJxF1"},"source":[""],"execution_count":null,"outputs":[]},{"cell_type":"code","metadata":{"id":"q8mhOm5yJxDK"},"source":[""],"execution_count":null,"outputs":[]},{"cell_type":"code","metadata":{"id":"xenQUBtTJxAg"},"source":[""],"execution_count":null,"outputs":[]},{"cell_type":"code","metadata":{"id":"s4WYp3MfJw-S"},"source":[""],"execution_count":null,"outputs":[]},{"cell_type":"code","metadata":{"id":"IMkdjOB5Jw75"},"source":[""],"execution_count":null,"outputs":[]},{"cell_type":"code","metadata":{"id":"D8iDjDMYJw6E"},"source":[""],"execution_count":null,"outputs":[]},{"cell_type":"code","metadata":{"id":"SR7JN30LJw39"},"source":[""],"execution_count":null,"outputs":[]},{"cell_type":"code","metadata":{"id":"qQqZSDG2Jw1t"},"source":[""],"execution_count":null,"outputs":[]},{"cell_type":"code","metadata":{"id":"62YeaemxJw0Z"},"source":[""],"execution_count":null,"outputs":[]},{"cell_type":"code","metadata":{"id":"oRi6iTOWJwxm"},"source":[""],"execution_count":null,"outputs":[]},{"cell_type":"code","metadata":{"id":"5K4pu5QTJwt9"},"source":[""],"execution_count":null,"outputs":[]},{"cell_type":"code","metadata":{"id":"plj_weLqJwr8"},"source":[""],"execution_count":null,"outputs":[]},{"cell_type":"code","metadata":{"id":"IgchEQSFJwpb"},"source":[""],"execution_count":null,"outputs":[]},{"cell_type":"code","metadata":{"id":"_tfpMq_QJwnh"},"source":[""],"execution_count":null,"outputs":[]},{"cell_type":"code","metadata":{"id":"jgObcf7tJwlp"},"source":[""],"execution_count":null,"outputs":[]},{"cell_type":"code","metadata":{"id":"I4z7gr8rJwj6"},"source":[""],"execution_count":null,"outputs":[]},{"cell_type":"code","metadata":{"id":"T04z2hYwJwiN"},"source":[""],"execution_count":null,"outputs":[]},{"cell_type":"code","metadata":{"id":"M6MZgcrqJwgm"},"source":[""],"execution_count":null,"outputs":[]},{"cell_type":"code","metadata":{"id":"Z1DwQY30Jwev"},"source":[""],"execution_count":null,"outputs":[]},{"cell_type":"code","metadata":{"id":"rfU-6-FmJwYo"},"source":[""],"execution_count":null,"outputs":[]},{"cell_type":"code","metadata":{"id":"Ahpn5sKcJwV4"},"source":[""],"execution_count":null,"outputs":[]},{"cell_type":"code","metadata":{"id":"99uRxq0sJwUG"},"source":[""],"execution_count":null,"outputs":[]},{"cell_type":"code","metadata":{"id":"X7ZdQJnhJwSp"},"source":[""],"execution_count":null,"outputs":[]},{"cell_type":"code","metadata":{"id":"XdCdU7BLJwRM"},"source":[""],"execution_count":null,"outputs":[]},{"cell_type":"code","metadata":{"id":"-D61XxOZJwMC"},"source":[""],"execution_count":null,"outputs":[]},{"cell_type":"code","metadata":{"id":"2uYPlL8kJwJp"},"source":[""],"execution_count":null,"outputs":[]},{"cell_type":"code","metadata":{"id":"NjVcTcYlJwIA"},"source":[""],"execution_count":null,"outputs":[]},{"cell_type":"code","metadata":{"id":"hGJZnvbiJvij"},"source":[""],"execution_count":null,"outputs":[]}]}