my-first-machine-learning-model-level-1.ipynb

{"cells":[{"metadata":{"_cell_guid":"e81ee64d-e474-4662-9036-ce23df615199","_uuid":"b6269c0e8f417f82daf093dda8fa0da6d2c57d86"},"cell_type":"markdown","source":"# Intro\n**This is your workspace for Kaggle's Machine Learning course**\n\nYou will build and continually improve a model to predict housing prices as you work through each tutorial.\n\nThe tutorials you read use data from Melbourne. The Melbourne data is not available in this workspace.  Instead, you will translate the concepts to work with the data in this notebook, the Iowa data.\n\n# Write Your Code Below\n"},{"metadata":{"_cell_guid":"86b26423-563a-4fa1-a595-89e25ff93089","_uuid":"1c728098629e1301643443b1341556a15c089b2b","trusted":true},"cell_type":"code","source":"import numpy as np\nimport pandas as pd\nfrom sklearn.tree import DecisionTreeRegressor\nfrom sklearn.ensemble import RandomForestRegressor\nfrom sklearn.metrics import mean_absolute_error\nfrom sklearn.model_selection import train_test_split\n\nohio_file_path = '../input/train.csv' # this is the path to the Iowa data that you will use\nohio_data = pd.read_csv(ohio_file_path)\nprint(ohio_data.columns)\n\nohio_price_data = ohio_data.SalePrice\nprint(ohio_price_data.head())\n\ncolumn_of_interest = ['LotArea', 'YearBuilt', '1stFlrSF', '2ndFlrSF', 'FullBath', 'BedroomAbvGr', 'TotRmsAbvGrd']\ntwo_columns_of_data = ohio_data[column_of_interest]\ntwo_columns_of_data.describe()\n\nfiltered_ohio_data = ohio_data.dropna(axis=0)\ny = ohio_data.SalePrice\nohio_predictors = ['LotArea', 'YearBuilt', '1stFlrSF', '2ndFlrSF', 'FullBath', 'BedroomAbvGr', 'TotRmsAbvGrd']\nX = ohio_data[ohio_predictors]\nohio_model = DecisionTreeRegressor()\nohio_model.fit(X, y)\nprint(\"Making predictions for the following 5 houses:\")\nprint(X.head())\nprint(\"The predictions are\")\nprint(ohio_model.predict(X.head()))\npredicted_home_prices = ohio_model.predict(X)\nmean_absolute_error(y, predicted_home_prices)\ntrain_X, val_X, train_y, val_y = train_test_split(X, y,random_state = 0)\nohio_model = DecisionTreeRegressor()\nohio_model.fit(train_X, train_y)\nval_predictions = ohio_model.predict(val_X)\nprint(mean_absolute_error(val_y, val_predictions))\ndef get_mae(max_leaf_nodes, predictors_train, predictors_val, targ_train, targ_val):\n    model = DecisionTreeRegressor(max_leaf_nodes=max_leaf_nodes, random_state=0)\n    model.fit(predictors_train, targ_train)\n    preds_val = model.predict(predictors_val)\n    mae = mean_absolute_error(targ_val, preds_val)\n    return(mae)\nfor max_leaf_nodes in [2, 20, 200, 2000]:\n    my_mae = get_mae(max_leaf_nodes, train_X, val_X, train_y, val_y)\n    print(\"Max leaf nodes: %d  \\t\\t Mean Absolute Error:  %d\" %(max_leaf_nodes, my_mae))\n    \n    forest_model = RandomForestRegressor()\n    forest_model.fit(train_X, train_y)\n    melb_preds = forest_model.predict(val_X)\n    print(mean_absolute_error(val_y, melb_preds))\n    \ntrain = pd.read_csv('../input/train.csv')\ntrain_y = train.SalePrice\npredictor_cols = ['LotArea', 'OverallQual', 'YearBuilt', 'TotRmsAbvGrd']\ntrain_X = train[predictor_cols]\n\nmy_model = RandomForestRegressor()\nmy_model.fit(train_X, train_y)\n\ntest = pd.read_csv('../input/test.csv')\ntest_X = test[predictor_cols]\npredicted_prices = my_model.predict(test_X)\nprint(predicted_prices)\n\nmy_submission = pd.DataFrame({'Id': test.Id, 'SalePrice': predicted_prices})\nmy_submission.to_csv('JRK_Level1.csv', index=False)","execution_count":22,"outputs":[]},{"metadata":{"_uuid":"5993d6bbf5e7c47ffc4e10dd0a3940783d861573"},"cell_type":"markdown","source":"\n**If you have any questions or hit any problems, come to the [Learn Discussion](https://www.kaggle.com/learn-forum) for help. **\n\n**Return to [ML Course Index](https://www.kaggle.com/learn/machine-learning)**"},{"metadata":{"_uuid":"04becaecca84c4b4d7e81563a64ba8c8a605fec4"},"cell_type":"markdown","source":""}],"metadata":{"kernelspec":{"display_name":"Python 3","language":"python","name":"python3"},"language_info":{"name":"python","version":"3.6.5","mimetype":"text/x-python","codemirror_mode":{"name":"ipython","version":3},"pygments_lexer":"ipython3","nbconvert_exporter":"python","file_extension":".py"}},"nbformat":4,"nbformat_minor":1}