notebook1bd25190cd.ipynb

{"metadata":{"kernelspec":{"language":"python","display_name":"Python 3","name":"python3"},"language_info":{"pygments_lexer":"ipython3","nbconvert_exporter":"python","version":"3.6.4","file_extension":".py","codemirror_mode":{"name":"ipython","version":3},"name":"python","mimetype":"text/x-python"}},"nbformat_minor":4,"nbformat":4,"cells":[{"cell_type":"code","source":"# This Python 3 environment comes with many helpful analytics libraries installed\n# It is defined by the kaggle/python Docker image: https://github.com/kaggle/docker-python\n# For example, here's several helpful packages to load\n\nimport numpy as np # linear algebra\nimport pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)\n\n# Input data files are available in the read-only \"../input/\" directory\n# For example, running this (by clicking run or pressing Shift+Enter) will list all files under the input directory\n\nimport os\nfor dirname, _, filenames in os.walk('/kaggle/input'):\n    for filename in filenames:\n        print(os.path.join(dirname, filename))\n\n# You can write up to 20GB to the current directory (/kaggle/working/) that gets preserved as output when you create a version using \"Save & Run All\" \n# You can also write temporary files to /kaggle/temp/, but they won't be saved outside of the current session","metadata":{"_uuid":"8f2839f25d086af736a60e9eeb907d3b93b6e0e5","_cell_guid":"b1076dfc-b9ad-4769-8c92-a6c4dae69d19","execution":{"iopub.status.busy":"2021-10-25T02:17:25.081757Z","iopub.execute_input":"2021-10-25T02:17:25.082275Z","iopub.status.idle":"2021-10-25T02:17:25.091415Z","shell.execute_reply.started":"2021-10-25T02:17:25.082243Z","shell.execute_reply":"2021-10-25T02:17:25.090685Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"markdown","source":"This notebook was created by Will Ortiz","metadata":{}},{"cell_type":"code","source":"# Loading data\ntrain_data = pd.read_csv(\"/kaggle/input/cap-4611-2021-fall-assignment-02/train.csv\")\ntest_data = pd.read_csv(\"/kaggle/input/cap-4611-2021-fall-assignment-02/eval.csv\")\ntrain_data.head()","metadata":{"execution":{"iopub.status.busy":"2021-10-25T02:17:25.09298Z","iopub.execute_input":"2021-10-25T02:17:25.093615Z","iopub.status.idle":"2021-10-25T02:17:25.139375Z","shell.execute_reply.started":"2021-10-25T02:17:25.093571Z","shell.execute_reply":"2021-10-25T02:17:25.138284Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"# Checking for missing values\ntrain_data.isnull().sum()","metadata":{"execution":{"iopub.status.busy":"2021-10-25T02:17:25.143115Z","iopub.execute_input":"2021-10-25T02:17:25.143468Z","iopub.status.idle":"2021-10-25T02:17:25.15318Z","shell.execute_reply.started":"2021-10-25T02:17:25.143424Z","shell.execute_reply":"2021-10-25T02:17:25.152053Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"# Checking for missing values\ntest_data.isnull().sum()","metadata":{"execution":{"iopub.status.busy":"2021-10-25T02:17:25.155265Z","iopub.execute_input":"2021-10-25T02:17:25.15575Z","iopub.status.idle":"2021-10-25T02:17:25.171249Z","shell.execute_reply.started":"2021-10-25T02:17:25.155719Z","shell.execute_reply":"2021-10-25T02:17:25.169963Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"# Dropping irrelevant columns such as console, title, and id\nX_train = train_data.drop([\"console\", \"title\", \"id\", \"esrb_rating\"], axis = 1)\nX_test = test_data.drop([\"console\", \"id\"], axis = 1)\ny_train = train_data[\"esrb_rating\"]","metadata":{"execution":{"iopub.status.busy":"2021-10-25T02:17:25.172446Z","iopub.execute_input":"2021-10-25T02:17:25.173626Z","iopub.status.idle":"2021-10-25T02:17:25.182014Z","shell.execute_reply.started":"2021-10-25T02:17:25.173563Z","shell.execute_reply":"2021-10-25T02:17:25.180984Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"# Logistic regression model with evaluation\nfrom sklearn.linear_model import LogisticRegression\nfrom sklearn.model_selection import cross_val_score\nlogreg = LogisticRegression()\nlogreg.fit(X_train,y_train)\ny_pred = logreg.predict(X_test)\nprint(cross_val_score(logreg, X_train, y_train, cv=5).mean())\nprint(pd.DataFrame(y_pred).describe())","metadata":{"execution":{"iopub.status.busy":"2021-10-25T02:17:25.183254Z","iopub.execute_input":"2021-10-25T02:17:25.183984Z","iopub.status.idle":"2021-10-25T02:17:25.485907Z","shell.execute_reply.started":"2021-10-25T02:17:25.183949Z","shell.execute_reply":"2021-10-25T02:17:25.48484Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"# Support vector machine with evaluation and hyperparameters\nfrom sklearn import svm\nfrom sklearn.model_selection import GridSearchCV\nparam_grid = {\"C\": [0.1,1, 10, 100], \"gamma\": [1,0.1,0.01,0.001],\"kernel\": [\"rbf\", \"poly\", \"sigmoid\"]}\ngrid = GridSearchCV(svm.SVC(),param_grid,refit=True,verbose=1)\ngrid.fit(X_train,y_train)\nestimator = grid.best_estimator_\ngrid_predictions = estimator.predict(X_test)\nclf = svm.SVC()\nclf.fit(X_train, y_train)\ny_pred2 = clf.predict(X_test)\nprint(cross_val_score(clf, X_train, y_train, cv=5).mean())\nprint(cross_val_score(grid, X_train, y_train, cv=5).mean())\nprint(pd.DataFrame(y_pred2).describe())","metadata":{"execution":{"iopub.status.busy":"2021-10-25T02:17:25.487159Z","iopub.execute_input":"2021-10-25T02:17:25.487417Z","iopub.status.idle":"2021-10-25T02:18:57.326189Z","shell.execute_reply.started":"2021-10-25T02:17:25.48738Z","shell.execute_reply":"2021-10-25T02:18:57.325013Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"# Decision tree with evaluation\nfrom sklearn.tree import DecisionTreeClassifier\ndecision_tree = DecisionTreeClassifier()\ndecision_tree.fit(X_train, y_train)\ny_pred3 = decision_tree.predict(X_test)\nprint(cross_val_score(decision_tree, X_train, y_train, cv=5).mean())\nprint(pd.DataFrame(y_pred3).describe())","metadata":{"execution":{"iopub.status.busy":"2021-10-25T02:18:57.328308Z","iopub.execute_input":"2021-10-25T02:18:57.328661Z","iopub.status.idle":"2021-10-25T02:18:57.39346Z","shell.execute_reply.started":"2021-10-25T02:18:57.328617Z","shell.execute_reply":"2021-10-25T02:18:57.392532Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"# Random forest with evaluation\nfrom sklearn.ensemble import RandomForestClassifier\nrandom_forest = RandomForestClassifier(n_estimators=10)\nrandom_forest.fit(X_train, y_train)\ny_pred4 = random_forest.predict(X_test)\nprint(cross_val_score(random_forest, X_train, y_train, cv=5).mean())\nprint(pd.DataFrame(y_pred4).describe())","metadata":{"execution":{"iopub.status.busy":"2021-10-25T02:18:57.395032Z","iopub.execute_input":"2021-10-25T02:18:57.395374Z","iopub.status.idle":"2021-10-25T02:18:57.580204Z","shell.execute_reply.started":"2021-10-25T02:18:57.395311Z","shell.execute_reply":"2021-10-25T02:18:57.578868Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"# KNeighbors with evaluation\nfrom sklearn.neighbors import KNeighborsClassifier\nneighbors = KNeighborsClassifier(n_neighbors=5)\nneighbors.fit(X_train, y_train)\ny_pred5 = neighbors.predict(X_test)\nprint(cross_val_score(neighbors, X_train, y_train, cv=5).mean())\nprint(pd.DataFrame(y_pred5).describe())","metadata":{"execution":{"iopub.status.busy":"2021-10-25T02:18:57.582447Z","iopub.execute_input":"2021-10-25T02:18:57.582678Z","iopub.status.idle":"2021-10-25T02:18:57.827401Z","shell.execute_reply.started":"2021-10-25T02:18:57.582651Z","shell.execute_reply":"2021-10-25T02:18:57.826191Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"output = pd.DataFrame({\"id\":test_data[\"id\"], \"esrb_rating\": y_pred2})\noutput.to_csv(\"submission.csv\", index=False)","metadata":{"execution":{"iopub.status.busy":"2021-10-25T02:18:57.828556Z","iopub.execute_input":"2021-10-25T02:18:57.828769Z","iopub.status.idle":"2021-10-25T02:18:57.836083Z","shell.execute_reply.started":"2021-10-25T02:18:57.828744Z","shell.execute_reply":"2021-10-25T02:18:57.835089Z"},"trusted":true},"execution_count":null,"outputs":[]}]}