Introduction to Machine Learning With Python

scikit1.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":"2023-12-17T17:51:20.921373Z","iopub.execute_input":"2023-12-17T17:51:20.921789Z","iopub.status.idle":"2023-12-17T17:51:35.052534Z","shell.execute_reply.started":"2023-12-17T17:51:20.921759Z","shell.execute_reply":"2023-12-17T17:51:35.051000Z"},"trusted":true},"execution_count":72,"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":"2023-12-17T17:51:35.055464Z","iopub.execute_input":"2023-12-17T17:51:35.055834Z","iopub.status.idle":"2023-12-17T17:51:35.062055Z","shell.execute_reply.started":"2023-12-17T17:51:35.055804Z","shell.execute_reply":"2023-12-17T17:51:35.061007Z"},"trusted":true},"execution_count":73,"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":"2023-12-17T17:51:35.063076Z","iopub.execute_input":"2023-12-17T17:51:35.063424Z","iopub.status.idle":"2023-12-17T17:51:35.104283Z","shell.execute_reply.started":"2023-12-17T17:51:35.063395Z","shell.execute_reply":"2023-12-17T17:51:35.103140Z"},"trusted":true},"execution_count":74,"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":"2023-12-17T17:51:35.107085Z","iopub.execute_input":"2023-12-17T17:51:35.107492Z","iopub.status.idle":"2023-12-17T17:51:35.124253Z","shell.execute_reply.started":"2023-12-17T17:51:35.107458Z","shell.execute_reply":"2023-12-17T17:51:35.123112Z"},"trusted":true},"execution_count":75,"outputs":[]},{"cell_type":"code","source":"print(dataset)","metadata":{"execution":{"iopub.status.busy":"2023-12-17T17:51:35.126007Z","iopub.execute_input":"2023-12-17T17:51:35.126546Z","iopub.status.idle":"2023-12-17T17:51:35.138858Z","shell.execute_reply.started":"2023-12-17T17:51:35.126497Z","shell.execute_reply":"2023-12-17T17:51:35.137945Z"},"trusted":true},"execution_count":76,"outputs":[{"name":"stdout","text":"[{'age': 10.73, 'origin': 'african', 'dsize': 4.53}, {'age': 11.65, 'origin': 'african', 'dsize': 3.51}, {'age': 10.13, 'origin': 'african', 'dsize': 5.08}, {'age': 11.84, 'origin': 'african', 'dsize': 4.2}, {'age': 11.83, 'origin': 'african', 'dsize': 4.52}, {'age': 11.62, 'origin': 'african', 'dsize': 4.98}, {'age': 10.13, 'origin': 'african', 'dsize': 3.61}, {'age': 11.91, 'origin': 'african', 'dsize': 4.17}, {'age': 11.46, 'origin': 'african', 'dsize': 3.74}, {'age': 11.35, 'origin': 'african', 'dsize': 3.46}, {'age': 11.57, 'origin': 'african', 'dsize': 3.6}, {'age': 11.14, 'origin': 'african', 'dsize': 2.7}, {'age': 11.37, 'origin': 'african', 'dsize': 4.27}, {'age': 11.87, 'origin': 'african', 'dsize': 3.58}, {'age': 11.92, 'origin': 'african', 'dsize': 4.12}, {'age': 11.35, 'origin': 'african', 'dsize': 2.74}, {'age': 10.96, 'origin': 'african', 'dsize': 4.18}, {'age': 11.79, 'origin': 'african', 'dsize': 4.55}, {'age': 11.3, 'origin': 'african', 'dsize': 3.41}, {'age': 10.08, 'origin': 'african', 'dsize': 3.62}, {'age': 10.8, 'origin': 'african', 'dsize': 2.68}, {'age': 11.43, 'origin': 'african', 'dsize': 4.13}, {'age': 11.92, 'origin': 'african', 'dsize': 2.78}, {'age': 11.14, 'origin': 'african', 'dsize': 3.47}, {'age': 11.47, 'origin': 'african', 'dsize': 3.47}, {'age': 10.95, 'origin': 'african', 'dsize': 4.37}, {'age': 10.62, 'origin': 'african', 'dsize': 4.79}, {'age': 10.78, 'origin': 'african', 'dsize': 4.6}, {'age': 11.81, 'origin': 'african', 'dsize': 3.67}, {'age': 10.31, 'origin': 'african', 'dsize': 3.61}, {'age': 11.84, 'origin': 'african', 'dsize': 3.84}, {'age': 11.5, 'origin': 'african', 'dsize': 3.63}, {'age': 11.64, 'origin': 'african', 'dsize': 2.76}, {'age': 10.4, 'origin': 'african', 'dsize': 4.8}, {'age': 10.36, 'origin': 'african', 'dsize': 4.23}, {'age': 10.05, 'origin': 'african', 'dsize': 3.45}, {'age': 10.54, 'origin': 'african', 'dsize': 4.56}, {'age': 10.78, 'origin': 'african', 'dsize': 3.19}, {'age': 10.3, 'origin': 'african', 'dsize': 3.82}, {'age': 10.16, 'origin': 'african', 'dsize': 4.97}, {'age': 10.63, 'origin': 'african', 'dsize': 2.66}, {'age': 11.58, 'origin': 'african', 'dsize': 4.55}, {'age': 11.33, 'origin': 'african', 'dsize': 4.51}, {'age': 10.54, 'origin': 'african', 'dsize': 4.04}, {'age': 11.94, 'origin': 'african', 'dsize': 3.04}, {'age': 10.71, 'origin': 'african', 'dsize': 4.45}, {'age': 11.54, 'origin': 'african', 'dsize': 2.76}, {'age': 10.06, 'origin': 'african', 'dsize': 4.34}, {'age': 11.51, 'origin': 'african', 'dsize': 2.77}, {'age': 11.24, 'origin': 'african', 'dsize': 4.14}, {'age': 10.6, 'origin': 'african', 'dsize': 2.79}, {'age': 10.96, 'origin': 'african', 'dsize': 5.05}, {'age': 10.47, 'origin': 'african', 'dsize': 4.9}, {'age': 11.59, 'origin': 'african', 'dsize': 3.57}, {'age': 11.37, 'origin': 'african', 'dsize': 4.05}, {'age': 11.86, 'origin': 'african', 'dsize': 4.23}, {'age': 10.12, 'origin': 'african', 'dsize': 4.3}, {'age': 11.62, 'origin': 'african', 'dsize': 5.03}, {'age': 10.18, 'origin': 'african', 'dsize': 5.07}, {'age': 11.47, 'origin': 'african', 'dsize': 4.32}, {'age': 11.83, 'origin': 'african', 'dsize': 4.41}, {'age': 11.8, 'origin': 'african', 'dsize': 2.6}, {'age': 10.14, 'origin': 'african', 'dsize': 4.98}, {'age': 11.32, 'origin': 'african', 'dsize': 2.9}, {'age': 10.85, 'origin': 'african', 'dsize': 4.7}, {'age': 10.47, 'origin': 'african', 'dsize': 2.98}, {'age': 10.87, 'origin': 'african', 'dsize': 3.46}, {'age': 11.86, 'origin': 'african', 'dsize': 4.61}, {'age': 10.09, 'origin': 'african', 'dsize': 3.54}, {'age': 11.51, 'origin': 'african', 'dsize': 4.99}, {'age': 11.09, 'origin': 'african', 'dsize': 3.13}, {'age': 10.1, 'origin': 'african', 'dsize': 4.89}, {'age': 10.06, 'origin': 'african', 'dsize': 5.0}, {'age': 11.7, 'origin': 'african', 'dsize': 4.28}, {'age': 11.63, 'origin': 'african', 'dsize': 4.7}, {'age': 11.43, 'origin': 'african', 'dsize': 4.57}, {'age': 10.79, 'origin': 'african', 'dsize': 3.17}, {'age': 10.59, 'origin': 'african', 'dsize': 2.92}, {'age': 11.2, 'origin': 'african', 'dsize': 2.6}, {'age': 11.08, 'origin': 'african', 'dsize': 2.99}, {'age': 11.68, 'origin': 'african', 'dsize': 3.56}, {'age': 11.51, 'origin': 'african', 'dsize': 3.82}, {'age': 11.2, 'origin': 'african', 'dsize': 4.76}, {'age': 11.37, 'origin': 'african', 'dsize': 4.74}, {'age': 11.23, 'origin': 'african', 'dsize': 3.69}, {'age': 10.88, 'origin': 'african', 'dsize': 4.31}, {'age': 11.24, 'origin': 'african', 'dsize': 3.0}, {'age': 10.05, 'origin': 'african', 'dsize': 3.67}, {'age': 11.82, 'origin': 'african', 'dsize': 3.63}, {'age': 11.7, 'origin': 'african', 'dsize': 4.3}, {'age': 10.08, 'origin': 'african', 'dsize': 4.37}, {'age': 11.06, 'origin': 'african', 'dsize': 4.81}, {'age': 11.64, 'origin': 'african', 'dsize': 3.37}, {'age': 10.41, 'origin': 'african', 'dsize': 3.43}, {'age': 10.15, 'origin': 'african', 'dsize': 3.05}, {'age': 10.73, 'origin': 'african', 'dsize': 3.34}, {'age': 10.77, 'origin': 'african', 'dsize': 5.08}, {'age': 10.22, 'origin': 'african', 'dsize': 2.8}, {'age': 10.99, 'origin': 'african', 'dsize': 3.2}, {'age': 10.56, 'origin': 'african', 'dsize': 3.08}, {'age': 12.67, 'origin': 'african', 'dsize': 5.06}, {'age': 12.34, 'origin': 'african', 'dsize': 5.12}, {'age': 12.82, 'origin': 'african', 'dsize': 5.9}, {'age': 12.03, 'origin': 'african', 'dsize': 4.86}, {'age': 12.07, 'origin': 'african', 'dsize': 5.53}, {'age': 12.49, 'origin': 'african', 'dsize': 5.15}, {'age': 12.95, 'origin': 'african', 'dsize': 4.66}, {'age': 12.9, 'origin': 'african', 'dsize': 4.68}, {'age': 12.83, 'origin': 'african', 'dsize': 4.46}, {'age': 12.04, 'origin': 'african', 'dsize': 4.64}, {'age': 12.87, 'origin': 'african', 'dsize': 4.72}, {'age': 12.35, 'origin': 'african', 'dsize': 5.84}, {'age': 12.16, 'origin': 'african', 'dsize': 4.46}, {'age': 12.72, 'origin': 'african', 'dsize': 5.97}, {'age': 12.62, 'origin': 'african', 'dsize': 5.86}, {'age': 12.11, 'origin': 'african', 'dsize': 4.82}, {'age': 12.01, 'origin': 'african', 'dsize': 4.34}, {'age': 12.23, 'origin': 'african', 'dsize': 5.11}, {'age': 12.59, 'origin': 'african', 'dsize': 4.94}, {'age': 12.75, 'origin': 'african', 'dsize': 5.22}, {'age': 12.81, 'origin': 'african', 'dsize': 5.1}, {'age': 12.53, 'origin': 'african', 'dsize': 5.07}, {'age': 12.66, 'origin': 'african', 'dsize': 5.33}, {'age': 12.19, 'origin': 'african', 'dsize': 5.74}, {'age': 12.19, 'origin': 'african', 'dsize': 5.86}, {'age': 12.33, 'origin': 'african', 'dsize': 5.22}, {'age': 12.27, 'origin': 'african', 'dsize': 5.62}, {'age': 12.46, 'origin': 'african', 'dsize': 4.19}, {'age': 12.33, 'origin': 'african', 'dsize': 5.51}, {'age': 12.43, 'origin': 'african', 'dsize': 4.95}, {'age': 12.26, 'origin': 'african', 'dsize': 4.01}, {'age': 12.51, 'origin': 'african', 'dsize': 4.3}, {'age': 12.78, 'origin': 'african', 'dsize': 4.77}, {'age': 12.54, 'origin': 'african', 'dsize': 4.8}, {'age': 12.01, 'origin': 'african', 'dsize': 5.48}, {'age': 12.85, 'origin': 'african', 'dsize': 5.51}, {'age': 12.24, 'origin': 'african', 'dsize': 5.45}, {'age': 12.6, 'origin': 'african', 'dsize': 4.84}, {'age': 12.77, 'origin': 'african', 'dsize': 4.22}, 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{'age': 33.22, 'origin': 'european', 'dsize': 4.99}, {'age': 38.13, 'origin': 'european', 'dsize': 5.82}, {'age': 39.79, 'origin': 'european', 'dsize': 5.21}, {'age': 40.27, 'origin': 'european', 'dsize': 6.22}, {'age': 40.82, 'origin': 'european', 'dsize': 4.83}, {'age': 42.45, 'origin': 'european', 'dsize': 6.33}, {'age': 49.09, 'origin': 'european', 'dsize': 4.44}, {'age': 45.45, 'origin': 'european', 'dsize': 4.31}, {'age': 44.7, 'origin': 'european', 'dsize': 4.78}, {'age': 51.81, 'origin': 'european', 'dsize': 5.14}, {'age': 60.21, 'origin': 'european', 'dsize': 4.99}, {'age': 60.63, 'origin': 'european', 'dsize': 6.28}, {'age': 56.54, 'origin': 'european', 'dsize': 5.36}, {'age': 56.3, 'origin': 'european', 'dsize': 6.01}, {'age': 69.78, 'origin': 'european', 'dsize': 6.56}, {'age': 68.16, 'origin': 'european', 'dsize': 4.32}, {'age': 63.39, 'origin': 'european', 'dsize': 5.2}, {'age': 66.72, 'origin': 'european', 'dsize': 5.12}, {'age': 67.4, 'origin': 'european', 'dsize': 4.8}, {'age': 95.46, 'origin': 'european', 'dsize': 6.23}, {'age': 96.49, 'origin': 'european', 'dsize': 4.52}, {'age': 75.33, 'origin': 'european', 'dsize': 5.87}, {'age': 99.16, 'origin': 'european', 'dsize': 5.85}, {'age': 92.31, 'origin': 'european', 'dsize': 4.85}]\n","output_type":"stream"}]},{"cell_type":"code","source":"from sklearn.linear_model import LinearRegression\nfrom sklearn.model_selection import train_test_split\nfrom pickle import dump\n\nflags = {\n    'european': 1,\n    'african': 2,\n}\n\ndef transform_data(data):\n    out = {'x': [], 'y': []}\n    for item in data:\n        out['x'].append([\n            item['age'],\n            flags[item['origin']]\n        ])\n        out['y'].append(item['dsize'])\n    return out\n\n_data = transform_data(dataset)\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\nX_train, X_test, y_train, y_test = train_test_split(_data['x'], _data['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":"2023-12-17T17:51:35.140036Z","iopub.execute_input":"2023-12-17T17:51:35.140850Z","iopub.status.idle":"2023-12-17T17:51:35.162460Z","shell.execute_reply.started":"2023-12-17T17:51:35.140816Z","shell.execute_reply":"2023-12-17T17:51:35.161490Z"},"trusted":true},"execution_count":77,"outputs":[{"name":"stdout","text":"0.35736310366652924\n","output_type":"stream"}]},{"cell_type":"code","source":"import pickle\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":"2023-12-17T17:51:35.164048Z","iopub.execute_input":"2023-12-17T17:51:35.164718Z","iopub.status.idle":"2023-12-17T17:51:35.174854Z","shell.execute_reply.started":"2023-12-17T17:51:35.164684Z","shell.execute_reply":"2023-12-17T17:51:35.173532Z"},"trusted":true},"execution_count":78,"outputs":[{"name":"stdout","text":"[3.94594695 4.10847885 4.55174768 6.16154429 6.3240762  6.11721741]\n","output_type":"stream"}]}]}