scikit3.ipynb

{"metadata":{"kernelspec":{"language":"python","display_name":"Python 3","name":"python3"},"language_info":{"name":"python","version":"3.10.12","mimetype":"text/x-python","codemirror_mode":{"name":"ipython","version":3},"pygments_lexer":"ipython3","nbconvert_exporter":"python","file_extension":".py"},"kaggle":{"accelerator":"none","dataSources":[],"dockerImageVersionId":30626,"isInternetEnabled":true,"language":"python","sourceType":"notebook","isGpuEnabled":false}},"nbformat_minor":4,"nbformat":4,"cells":[{"cell_type":"code","source":"!pip3 install numpy scipy matplotlib ipython scikit-learn pandas ","metadata":{"execution":{"iopub.status.busy":"2023-12-19T12:07:05.873328Z","iopub.execute_input":"2023-12-19T12:07:05.873933Z","iopub.status.idle":"2023-12-19T12:07:20.921658Z","shell.execute_reply.started":"2023-12-19T12:07:05.873893Z","shell.execute_reply":"2023-12-19T12:07:20.920168Z"},"trusted":true},"execution_count":5,"outputs":[{"name":"stdout","text":"Requirement already satisfied: numpy in /opt/conda/lib/python3.10/site-packages (1.24.3)\nRequirement already satisfied: scipy in /opt/conda/lib/python3.10/site-packages (1.11.4)\nRequirement already satisfied: matplotlib in /opt/conda/lib/python3.10/site-packages (3.7.4)\nRequirement already satisfied: ipython in /opt/conda/lib/python3.10/site-packages (8.14.0)\nRequirement already satisfied: scikit-learn in /opt/conda/lib/python3.10/site-packages (1.2.2)\nRequirement already satisfied: pandas in /opt/conda/lib/python3.10/site-packages (2.0.3)\nRequirement already satisfied: contourpy>=1.0.1 in /opt/conda/lib/python3.10/site-packages (from matplotlib) (1.1.0)\nRequirement already satisfied: cycler>=0.10 in /opt/conda/lib/python3.10/site-packages (from matplotlib) (0.11.0)\nRequirement already satisfied: fonttools>=4.22.0 in /opt/conda/lib/python3.10/site-packages (from matplotlib) (4.42.1)\nRequirement already satisfied: kiwisolver>=1.0.1 in /opt/conda/lib/python3.10/site-packages (from matplotlib) (1.4.4)\nRequirement already satisfied: packaging>=20.0 in /opt/conda/lib/python3.10/site-packages (from matplotlib) (21.3)\nRequirement already satisfied: pillow>=6.2.0 in /opt/conda/lib/python3.10/site-packages (from matplotlib) (10.1.0)\nRequirement already satisfied: pyparsing>=2.3.1 in /opt/conda/lib/python3.10/site-packages (from matplotlib) (3.0.9)\nRequirement already satisfied: python-dateutil>=2.7 in /opt/conda/lib/python3.10/site-packages (from matplotlib) (2.8.2)\nRequirement already satisfied: backcall in /opt/conda/lib/python3.10/site-packages (from ipython) (0.2.0)\nRequirement already satisfied: decorator in /opt/conda/lib/python3.10/site-packages (from ipython) (5.1.1)\nRequirement already satisfied: jedi>=0.16 in /opt/conda/lib/python3.10/site-packages (from ipython) (0.19.0)\nRequirement already satisfied: matplotlib-inline in /opt/conda/lib/python3.10/site-packages (from ipython) (0.1.6)\nRequirement already satisfied: pickleshare in /opt/conda/lib/python3.10/site-packages (from ipython) (0.7.5)\nRequirement already satisfied: prompt-toolkit!=3.0.37,<3.1.0,>=3.0.30 in /opt/conda/lib/python3.10/site-packages (from ipython) (3.0.39)\nRequirement already satisfied: pygments>=2.4.0 in /opt/conda/lib/python3.10/site-packages (from ipython) (2.16.1)\nRequirement already satisfied: stack-data in /opt/conda/lib/python3.10/site-packages (from ipython) (0.6.2)\nRequirement already satisfied: traitlets>=5 in /opt/conda/lib/python3.10/site-packages (from ipython) (5.9.0)\nRequirement already satisfied: pexpect>4.3 in /opt/conda/lib/python3.10/site-packages (from ipython) (4.8.0)\nRequirement already satisfied: joblib>=1.1.1 in /opt/conda/lib/python3.10/site-packages (from scikit-learn) (1.3.2)\nRequirement already satisfied: threadpoolctl>=2.0.0 in /opt/conda/lib/python3.10/site-packages (from scikit-learn) (3.2.0)\nRequirement already satisfied: pytz>=2020.1 in /opt/conda/lib/python3.10/site-packages (from pandas) (2023.3)\nRequirement already satisfied: tzdata>=2022.1 in /opt/conda/lib/python3.10/site-packages (from pandas) (2023.3)\nRequirement already satisfied: parso<0.9.0,>=0.8.3 in /opt/conda/lib/python3.10/site-packages (from jedi>=0.16->ipython) (0.8.3)\nRequirement already satisfied: ptyprocess>=0.5 in /opt/conda/lib/python3.10/site-packages (from pexpect>4.3->ipython) (0.7.0)\nRequirement already satisfied: wcwidth in /opt/conda/lib/python3.10/site-packages (from prompt-toolkit!=3.0.37,<3.1.0,>=3.0.30->ipython) (0.2.6)\nRequirement already satisfied: six>=1.5 in /opt/conda/lib/python3.10/site-packages (from python-dateutil>=2.7->matplotlib) (1.16.0)\nRequirement already satisfied: executing>=1.2.0 in /opt/conda/lib/python3.10/site-packages (from stack-data->ipython) (1.2.0)\nRequirement already satisfied: asttokens>=2.1.0 in /opt/conda/lib/python3.10/site-packages (from stack-data->ipython) (2.2.1)\nRequirement already satisfied: pure-eval in /opt/conda/lib/python3.10/site-packages (from stack-data->ipython) (0.2.2)\n","output_type":"stream"}]},{"cell_type":"code","source":"dataset = []","metadata":{"execution":{"iopub.status.busy":"2023-12-19T12:56:36.298035Z","iopub.execute_input":"2023-12-19T12:56:36.298544Z","iopub.status.idle":"2023-12-19T12:56:36.308716Z","shell.execute_reply.started":"2023-12-19T12:56:36.298505Z","shell.execute_reply":"2023-12-19T12:56:36.306685Z"},"trusted":true},"execution_count":43,"outputs":[]},{"cell_type":"code","source":"import numpy as np\n\n_d = [\n    {\"a_start\": 10, \"a_end\": 11.99, \"d_start\": 2.6, \"d_end\": 5.1},\n    {\"a_start\": 12, \"a_end\": 12.99, \"d_start\": 4.0, \"d_end\": 6.0},\n    {\"a_start\": 13, \"a_end\": 13.99, \"d_start\": 4.0, \"d_end\": 6.7},\n    {\"a_start\": 14, \"a_end\": 14.99, \"d_start\": 4.4, \"d_end\": 7.5},\n    {\"a_start\": 15, \"a_end\": 15.99, \"d_start\": 5.1, \"d_end\": 7.9},\n    {\"a_start\": 16, \"a_end\": 16.99, \"d_start\": 5.9, \"d_end\": 7.9},\n    {\"a_start\": 17, \"a_end\": 17.99, \"d_start\": 5.9, \"d_end\": 8.3},\n    {\"a_start\": 18, \"a_end\": 18.99, \"d_start\": 6.3, \"d_end\": 8.7},\n    {\"a_start\": 19, \"a_end\": 30.99, \"d_start\": 6.3, \"d_end\": 8.8},\n    {\"a_start\": 30, \"a_end\": 40.99, \"d_start\": 6.3, \"d_end\": 8.8},\n    {\"a_start\": 40, \"a_end\": 50.99, \"d_start\": 6.3, \"d_end\": 8.9},\n    {\"a_start\": 50, \"a_end\": 60.99, \"d_start\": 6.3, \"d_end\": 8.6},\n    {\"a_start\": 60, \"a_end\": 70.99, \"d_start\": 6.3, \"d_end\": 8.9},\n    {\"a_start\": 70, \"a_end\": 100.99, \"d_start\": 6.3, \"d_end\": 8.9}\n]\n\nfor x in _d:\n    # Generate 100 random ages between 13 and 13.99\n    ages = np.random.uniform(x[\"a_start\"], x[\"a_end\"], 100)\n\n    # Generate random dick sizes within the specified range for each age\n    dick_sizes = np.random.uniform(x[\"d_start\"], x[\"d_end\"], 100)\n\n    # Combine age and dick size into a single array\n    data = np.c_[ages.reshape(-1, 1), dick_sizes.reshape(-1, 1)]\n\n    # Convert numpy array to a list of dictionaries\n    data_list = [{'age': round(age,2), 'origin': 'african', 'dsize': round(dick_size,2)} for age, dick_size in zip(ages, dick_sizes)]\n\n    for v in data_list:\n        dataset.append(v)","metadata":{"execution":{"iopub.status.busy":"2023-12-19T12:56:37.622342Z","iopub.execute_input":"2023-12-19T12:56:37.622847Z","iopub.status.idle":"2023-12-19T12:56:37.665498Z","shell.execute_reply.started":"2023-12-19T12:56:37.622807Z","shell.execute_reply":"2023-12-19T12:56:37.663885Z"},"trusted":true},"execution_count":44,"outputs":[]},{"cell_type":"code","source":"import numpy as np\n\n_d = [\n    {\"a_start\": 10, \"a_end\": 11.99, \"d_start\": 1.6, \"d_end\": 3.1},\n    {\"a_start\": 12, \"a_end\": 12.99, \"d_start\": 2.0, \"d_end\": 4.0},\n    {\"a_start\": 13, \"a_end\": 13.99, \"d_start\": 2.0, \"d_end\": 4.7},\n    {\"a_start\": 14, \"a_end\": 14.99, \"d_start\": 2.4, \"d_end\": 5.5},\n    {\"a_start\": 15, \"a_end\": 15.99, \"d_start\": 3.1, \"d_end\": 5.9},\n    {\"a_start\": 16, \"a_end\": 16.99, \"d_start\": 3.9, \"d_end\": 5.9},\n    {\"a_start\": 17, \"a_end\": 17.99, \"d_start\": 3.9, \"d_end\": 6.3},\n    {\"a_start\": 18, \"a_end\": 18.99, \"d_start\": 4.3, \"d_end\": 6.7},\n    {\"a_start\": 19, \"a_end\": 30.99, \"d_start\": 4.3, \"d_end\": 6.8},\n    {\"a_start\": 30, \"a_end\": 40.99, \"d_start\": 4.3, \"d_end\": 6.8},\n    {\"a_start\": 40, \"a_end\": 50.99, \"d_start\": 4.3, \"d_end\": 6.9},\n    {\"a_start\": 50, \"a_end\": 60.99, \"d_start\": 4.3, \"d_end\": 6.6},\n    {\"a_start\": 60, \"a_end\": 70.99, \"d_start\": 4.3, \"d_end\": 6.9},\n    {\"a_start\": 70, \"a_end\": 100.99, \"d_start\": 4.3, \"d_end\": 6.9}\n]\n\nfor x in _d:\n    # Generate 100 random ages between 13 and 13.99\n    ages = np.random.uniform(x[\"a_start\"], x[\"a_end\"], 5)\n\n    # Generate random dick sizes within the specified range for each age\n    dick_sizes = np.random.uniform(x[\"d_start\"], x[\"d_end\"], 5)\n\n    # Combine age and dick size into a single array\n    data = np.c_[ages.reshape(-1, 1), dick_sizes.reshape(-1, 1)]\n\n    # Convert numpy array to a list of dictionaries\n    data_list = [{'age': round(age,2), 'origin': 'european', 'dsize': round(dick_size,2)} for age, dick_size in zip(ages, dick_sizes)]\n\n    for v in data_list:\n        dataset.append(v)","metadata":{"execution":{"iopub.status.busy":"2023-12-19T12:56:40.538730Z","iopub.execute_input":"2023-12-19T12:56:40.539237Z","iopub.status.idle":"2023-12-19T12:56:40.557072Z","shell.execute_reply.started":"2023-12-19T12:56:40.539197Z","shell.execute_reply":"2023-12-19T12:56:40.555952Z"},"trusted":true},"execution_count":45,"outputs":[]},{"cell_type":"code","source":"print(dataset)","metadata":{"execution":{"iopub.status.busy":"2023-12-19T12:56:46.354647Z","iopub.execute_input":"2023-12-19T12:56:46.355099Z","iopub.status.idle":"2023-12-19T12:56:46.366880Z","shell.execute_reply.started":"2023-12-19T12:56:46.355065Z","shell.execute_reply":"2023-12-19T12:56:46.365370Z"},"trusted":true},"execution_count":46,"outputs":[{"name":"stdout","text":"[{'age': 11.85, 'origin': 'african', 'dsize': 3.86}, {'age': 11.05, 'origin': 'african', 'dsize': 3.69}, {'age': 11.94, 'origin': 'african', 'dsize': 5.04}, {'age': 10.6, 'origin': 'african', 'dsize': 3.8}, {'age': 10.9, 'origin': 'african', 'dsize': 3.83}, {'age': 10.53, 'origin': 'african', 'dsize': 3.19}, {'age': 10.76, 'origin': 'african', 'dsize': 3.02}, {'age': 10.41, 'origin': 'african', 'dsize': 4.59}, {'age': 11.73, 'origin': 'african', 'dsize': 3.98}, {'age': 10.85, 'origin': 'african', 'dsize': 3.42}, {'age': 11.52, 'origin': 'african', 'dsize': 4.18}, {'age': 10.9, 'origin': 'african', 'dsize': 3.25}, {'age': 11.63, 'origin': 'african', 'dsize': 4.14}, {'age': 11.18, 'origin': 'african', 'dsize': 4.74}, {'age': 11.43, 'origin': 'african', 'dsize': 5.02}, {'age': 10.34, 'origin': 'african', 'dsize': 3.64}, {'age': 11.18, 'origin': 'african', 'dsize': 3.25}, {'age': 11.26, 'origin': 'african', 'dsize': 3.88}, {'age': 10.49, 'origin': 'african', 'dsize': 4.88}, {'age': 10.77, 'origin': 'african', 'dsize': 4.79}, {'age': 10.07, 'origin': 'african', 'dsize': 2.69}, {'age': 11.14, 'origin': 'african', 'dsize': 3.01}, {'age': 10.01, 'origin': 'african', 'dsize': 4.75}, {'age': 11.36, 'origin': 'african', 'dsize': 2.93}, {'age': 11.71, 'origin': 'african', 'dsize': 3.1}, {'age': 11.28, 'origin': 'african', 'dsize': 3.51}, {'age': 10.61, 'origin': 'african', 'dsize': 4.86}, {'age': 10.79, 'origin': 'african', 'dsize': 4.12}, {'age': 10.85, 'origin': 'african', 'dsize': 2.78}, {'age': 11.9, 'origin': 'african', 'dsize': 3.16}, {'age': 10.22, 'origin': 'african', 'dsize': 2.84}, {'age': 10.15, 'origin': 'african', 'dsize': 2.61}, {'age': 11.23, 'origin': 'african', 'dsize': 2.81}, {'age': 10.37, 'origin': 'african', 'dsize': 4.82}, {'age': 10.05, 'origin': 'african', 'dsize': 3.91}, {'age': 11.96, 'origin': 'african', 'dsize': 4.97}, {'age': 10.09, 'origin': 'african', 'dsize': 4.1}, {'age': 11.9, 'origin': 'african', 'dsize': 2.69}, {'age': 11.04, 'origin': 'african', 'dsize': 4.94}, {'age': 11.48, 'origin': 'african', 'dsize': 2.62}, {'age': 11.66, 'origin': 'african', 'dsize': 4.1}, {'age': 10.33, 'origin': 'african', 'dsize': 2.78}, {'age': 10.43, 'origin': 'african', 'dsize': 2.79}, {'age': 11.88, 'origin': 'african', 'dsize': 3.8}, {'age': 11.33, 'origin': 'african', 'dsize': 3.82}, {'age': 11.68, 'origin': 'african', 'dsize': 3.85}, {'age': 10.64, 'origin': 'african', 'dsize': 4.3}, {'age': 10.13, 'origin': 'african', 'dsize': 3.27}, {'age': 11.1, 'origin': 'african', 'dsize': 4.09}, {'age': 11.52, 'origin': 'african', 'dsize': 4.12}, {'age': 11.15, 'origin': 'african', 'dsize': 3.44}, {'age': 11.58, 'origin': 'african', 'dsize': 4.26}, {'age': 11.72, 'origin': 'african', 'dsize': 5.07}, {'age': 11.5, 'origin': 'african', 'dsize': 3.78}, {'age': 10.97, 'origin': 'african', 'dsize': 3.87}, {'age': 11.72, 'origin': 'african', 'dsize': 3.9}, {'age': 11.79, 'origin': 'african', 'dsize': 2.6}, {'age': 11.13, 'origin': 'african', 'dsize': 3.54}, {'age': 11.87, 'origin': 'african', 'dsize': 4.36}, {'age': 11.93, 'origin': 'african', 'dsize': 5.08}, {'age': 11.97, 'origin': 'african', 'dsize': 2.84}, {'age': 11.05, 'origin': 'african', 'dsize': 4.14}, {'age': 11.21, 'origin': 'african', 'dsize': 4.52}, {'age': 11.79, 'origin': 'african', 'dsize': 3.59}, {'age': 10.72, 'origin': 'african', 'dsize': 5.01}, {'age': 11.05, 'origin': 'african', 'dsize': 3.84}, {'age': 11.86, 'origin': 'african', 'dsize': 3.54}, {'age': 11.75, 'origin': 'african', 'dsize': 2.86}, {'age': 11.94, 'origin': 'african', 'dsize': 3.34}, {'age': 10.42, 'origin': 'african', 'dsize': 4.2}, {'age': 10.61, 'origin': 'african', 'dsize': 2.66}, {'age': 11.17, 'origin': 'african', 'dsize': 2.75}, {'age': 11.66, 'origin': 'african', 'dsize': 4.68}, {'age': 11.57, 'origin': 'african', 'dsize': 4.8}, {'age': 11.42, 'origin': 'african', 'dsize': 2.72}, {'age': 10.6, 'origin': 'african', 'dsize': 4.39}, {'age': 10.63, 'origin': 'african', 'dsize': 3.85}, {'age': 11.08, 'origin': 'african', 'dsize': 3.03}, {'age': 11.72, 'origin': 'african', 'dsize': 4.27}, {'age': 11.86, 'origin': 'african', 'dsize': 3.53}, {'age': 10.53, 'origin': 'african', 'dsize': 4.94}, {'age': 10.87, 'origin': 'african', 'dsize': 4.93}, {'age': 11.54, 'origin': 'african', 'dsize': 4.04}, {'age': 10.75, 'origin': 'african', 'dsize': 3.35}, {'age': 10.33, 'origin': 'african', 'dsize': 2.73}, {'age': 11.51, 'origin': 'african', 'dsize': 3.84}, {'age': 10.66, 'origin': 'african', 'dsize': 3.51}, {'age': 11.83, 'origin': 'african', 'dsize': 4.74}, {'age': 10.83, 'origin': 'african', 'dsize': 2.69}, {'age': 10.96, 'origin': 'african', 'dsize': 3.72}, {'age': 10.59, 'origin': 'african', 'dsize': 4.79}, {'age': 10.11, 'origin': 'african', 'dsize': 4.76}, {'age': 10.12, 'origin': 'african', 'dsize': 3.84}, {'age': 11.32, 'origin': 'african', 'dsize': 3.97}, {'age': 10.55, 'origin': 'african', 'dsize': 4.3}, {'age': 11.43, 'origin': 'african', 'dsize': 3.65}, {'age': 11.25, 'origin': 'african', 'dsize': 2.8}, {'age': 11.06, 'origin': 'african', 'dsize': 4.47}, {'age': 10.68, 'origin': 'african', 'dsize': 3.14}, {'age': 11.49, 'origin': 'african', 'dsize': 2.89}, {'age': 12.34, 'origin': 'african', 'dsize': 5.14}, {'age': 12.67, 'origin': 'african', 'dsize': 4.97}, {'age': 12.45, 'origin': 'african', 'dsize': 4.09}, {'age': 12.76, 'origin': 'african', 'dsize': 4.25}, {'age': 12.29, 'origin': 'african', 'dsize': 5.2}, {'age': 12.46, 'origin': 'african', 'dsize': 5.62}, {'age': 12.82, 'origin': 'african', 'dsize': 4.15}, {'age': 12.95, 'origin': 'african', 'dsize': 5.27}, {'age': 12.84, 'origin': 'african', 'dsize': 5.26}, {'age': 12.89, 'origin': 'african', 'dsize': 4.35}, {'age': 12.21, 'origin': 'african', 'dsize': 5.34}, {'age': 12.92, 'origin': 'african', 'dsize': 4.34}, {'age': 12.98, 'origin': 'african', 'dsize': 5.3}, {'age': 12.56, 'origin': 'african', 'dsize': 5.59}, {'age': 12.02, 'origin': 'african', 'dsize': 4.93}, {'age': 12.55, 'origin': 'african', 'dsize': 4.75}, {'age': 12.28, 'origin': 'african', 'dsize': 4.63}, {'age': 12.9, 'origin': 'african', 'dsize': 5.37}, {'age': 12.42, 'origin': 'african', 'dsize': 5.69}, {'age': 12.69, 'origin': 'african', 'dsize': 6.0}, {'age': 12.94, 'origin': 'african', 'dsize': 4.17}, {'age': 12.16, 'origin': 'african', 'dsize': 5.02}, {'age': 12.39, 'origin': 'african', 'dsize': 5.5}, {'age': 12.74, 'origin': 'african', 'dsize': 5.54}, {'age': 12.24, 'origin': 'african', 'dsize': 4.32}, {'age': 12.25, 'origin': 'african', 'dsize': 5.74}, {'age': 12.47, 'origin': 'african', 'dsize': 5.63}, {'age': 12.87, 'origin': 'african', 'dsize': 4.53}, {'age': 12.9, 'origin': 'african', 'dsize': 5.18}, {'age': 12.14, 'origin': 'african', 'dsize': 5.04}, {'age': 12.49, 'origin': 'african', 'dsize': 4.57}, {'age': 12.31, 'origin': 'african', 'dsize': 5.72}, {'age': 12.05, 'origin': 'african', 'dsize': 4.69}, {'age': 12.45, 'origin': 'african', 'dsize': 5.29}, {'age': 12.68, 'origin': 'african', 'dsize': 4.67}, {'age': 12.77, 'origin': 'african', 'dsize': 4.04}, {'age': 12.88, 'origin': 'african', 'dsize': 5.05}, {'age': 12.84, 'origin': 'african', 'dsize': 5.96}, {'age': 12.92, 'origin': 'african', 'dsize': 5.15}, {'age': 12.15, 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33.3, 'origin': 'european', 'dsize': 6.33}, {'age': 38.28, 'origin': 'european', 'dsize': 4.59}, {'age': 33.83, 'origin': 'european', 'dsize': 6.41}, {'age': 50.36, 'origin': 'european', 'dsize': 5.06}, {'age': 43.73, 'origin': 'european', 'dsize': 6.44}, {'age': 41.85, 'origin': 'european', 'dsize': 4.5}, {'age': 43.86, 'origin': 'european', 'dsize': 4.61}, {'age': 48.74, 'origin': 'european', 'dsize': 5.53}, {'age': 58.12, 'origin': 'european', 'dsize': 5.1}, {'age': 57.47, 'origin': 'european', 'dsize': 5.89}, {'age': 57.8, 'origin': 'european', 'dsize': 5.62}, {'age': 53.74, 'origin': 'european', 'dsize': 5.25}, {'age': 52.83, 'origin': 'european', 'dsize': 4.96}, {'age': 62.56, 'origin': 'european', 'dsize': 4.65}, {'age': 66.29, 'origin': 'european', 'dsize': 5.22}, {'age': 62.61, 'origin': 'european', 'dsize': 6.57}, {'age': 63.94, 'origin': 'european', 'dsize': 5.15}, {'age': 69.41, 'origin': 'european', 'dsize': 6.72}, {'age': 76.94, 'origin': 'european', 'dsize': 6.51}, {'age': 94.64, 'origin': 'european', 'dsize': 6.21}, {'age': 98.56, 'origin': 'european', 'dsize': 4.6}, {'age': 72.04, 'origin': 'european', 'dsize': 6.74}, {'age': 72.46, 'origin': 'european', 'dsize': 6.32}]\n","output_type":"stream"}]},{"cell_type":"code","source":"import pandas as pd\n\ndata = []\ntarget = []\ntarget_names = [\"dsize\"]\nfeature_names =[\"age\", \"origin\"]\n\nflags = {\n    'european': 0,\n    'african': 1,\n}\n\nfor item in dataset:\n    target.append(item[\"dsize\"])\n    data.append([item[\"age\"], flags[item[\"origin\"]]])\n\ndf = pd.DataFrame.from_records(data, columns=feature_names)\ndf['target'] = target\n\nX = df.drop('target', axis=1).values.tolist()\ny = list(df['target'])\n","metadata":{"execution":{"iopub.status.busy":"2023-12-19T12:57:08.904466Z","iopub.execute_input":"2023-12-19T12:57:08.905034Z","iopub.status.idle":"2023-12-19T12:57:08.925750Z","shell.execute_reply.started":"2023-12-19T12:57:08.904995Z","shell.execute_reply":"2023-12-19T12:57:08.923952Z"},"trusted":true},"execution_count":48,"outputs":[]},{"cell_type":"code","source":"from sklearn.linear_model import LinearRegression\nfrom sklearn.model_selection import train_test_split\nfrom pickle import dump\n\nX_train, X_test, y_train, y_test = train_test_split(X, y)\n\n# Train the model\nmodel = LinearRegression()\nmodel.fit(X_train, y_train)\n\ny_pred = model.predict(X_test)\naccuracy = model.score(X_test, y_test)\n\nprint(accuracy)\n\ndump(model, open('model.pkl', 'wb'))","metadata":{"execution":{"iopub.status.busy":"2023-12-19T12:57:11.069997Z","iopub.execute_input":"2023-12-19T12:57:11.070444Z","iopub.status.idle":"2023-12-19T12:57:11.089277Z","shell.execute_reply.started":"2023-12-19T12:57:11.070412Z","shell.execute_reply":"2023-12-19T12:57:11.087807Z"},"trusted":true},"execution_count":49,"outputs":[{"name":"stdout","text":"0.27405457614093787\n","output_type":"stream"}]},{"cell_type":"code","source":"import pickle\n\ndef transform_inputs(inputs):\n    ins = []\n    for inp in inputs:\n        ins.append([\n            inp['age'],\n            flags[inp['origin']]\n        ])\n    return ins\n\n# Open the file in binary read mode\nwith open('model.pkl', 'rb') as file:\n    # Load the object from the file\n    mdl = pickle.load(file)\n    \n    print(mdl.predict(transform_inputs([\n        {\n            'age': 11.5,\n            'origin': 'european',\n        },\n        {\n            'age': 17,\n            'origin': 'european',\n        },\n        {\n            'age': 32,\n            'origin': 'european',\n        },\n        {\n            'age': 11.5,\n            'origin': 'african',\n        },\n        {\n            'age': 17,\n            'origin': 'african',\n        },\n        {\n            'age': 10,\n            'origin': 'african',\n        },\n    ])))","metadata":{"execution":{"iopub.status.busy":"2023-12-19T12:57:14.048160Z","iopub.execute_input":"2023-12-19T12:57:14.048988Z","iopub.status.idle":"2023-12-19T12:57:14.061056Z","shell.execute_reply.started":"2023-12-19T12:57:14.048949Z","shell.execute_reply":"2023-12-19T12:57:14.059396Z"},"trusted":true},"execution_count":50,"outputs":[{"name":"stdout","text":"[4.15370643 4.31983216 4.77290233 6.11075101 6.27687674 6.06544399]\n","output_type":"stream"}]}]}

https://www.kaggle.com/code/clivern/dsize-prediction-with-scikit