{"metadata":{"kernelspec":{"language":"python","display_name":"Python 3","name":"python3"},"language_info":{"name":"python","version":"3.10.13","mimetype":"text/x-python","codemirror_mode":{"name":"ipython","version":3},"pygments_lexer":"ipython3","nbconvert_exporter":"python","file_extension":".py"},"kaggle":{"accelerator":"none","dataSources":[],"dockerImageVersionId":30673,"isInternetEnabled":true,"language":"python","sourceType":"notebook","isGpuEnabled":false}},"nbformat_minor":4,"nbformat":4,"cells":[{"cell_type":"code","source":"import pandas as pd\nimport numpy as np\n\n# Define the specifications for each column\nlevel = np.random.choice([200, 300, 400], 200000)\ncourse_units = np.random.randint(1, 4, 200000)\nattendance = np.random.randint(1, 11, 200000)\nmid_semester = np.random.randint(1, 21, 200000)\nassignments = np.random.randint(1, 11, 200000)\nexam = np.random.randint(1, 61, 200000)\n\n# Create a DataFrame with the generated data\ndata = {\n    'Level': level,\n    'Course Units': course_units,\n    'Attendance': attendance,\n    'Mid Semester': mid_semester,\n    'Assignments': assignments,\n    'Exam': exam\n}\n\ndf = pd.DataFrame(data)\n\n# Save the generated dataset to a CSV file\ndf.to_csv('generated_dataset.csv', index=False)","metadata":{"_uuid":"f1a5d48a-a556-437e-af80-a48817349c1e","_cell_guid":"1dcbfd44-e3b1-4850-bc84-2939cb202ddb","collapsed":false,"jupyter":{"outputs_hidden":false},"execution":{"iopub.status.busy":"2024-04-05T19:37:58.125621Z","iopub.execute_input":"2024-04-05T19:37:58.126183Z","iopub.status.idle":"2024-04-05T19:37:58.490623Z","shell.execute_reply.started":"2024-04-05T19:37:58.126132Z","shell.execute_reply":"2024-04-05T19:37:58.489436Z"},"trusted":true},"execution_count":32,"outputs":[]},{"cell_type":"code","source":"!pip install -U scikit-learn\n","metadata":{"execution":{"iopub.status.busy":"2024-04-05T19:37:58.492553Z","iopub.execute_input":"2024-04-05T19:37:58.492905Z","iopub.status.idle":"2024-04-05T19:38:17.175537Z","shell.execute_reply.started":"2024-04-05T19:37:58.492875Z","shell.execute_reply":"2024-04-05T19:38:17.173926Z"},"trusted":true},"execution_count":33,"outputs":[{"name":"stderr","text":"/opt/conda/lib/python3.10/pty.py:89: RuntimeWarning: os.fork() was called. os.fork() is incompatible with multithreaded code, and JAX is multithreaded, so this will likely lead to a deadlock.\n  pid, fd = os.forkpty()\n","output_type":"stream"},{"name":"stdout","text":"Requirement already satisfied: scikit-learn in /opt/conda/lib/python3.10/site-packages (1.2.2)\nCollecting scikit-learn\n  Downloading scikit_learn-1.4.1.post1-cp310-cp310-manylinux_2_17_x86_64.manylinux2014_x86_64.whl.metadata (11 kB)\nRequirement already satisfied: numpy<2.0,>=1.19.5 in /opt/conda/lib/python3.10/site-packages (from scikit-learn) (1.26.4)\nRequirement already satisfied: scipy>=1.6.0 in /opt/conda/lib/python3.10/site-packages (from scikit-learn) (1.11.4)\nRequirement already satisfied: joblib>=1.2.0 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)\nDownloading scikit_learn-1.4.1.post1-cp310-cp310-manylinux_2_17_x86_64.manylinux2014_x86_64.whl (12.1 MB)\n\u001b[2K   \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m12.1/12.1 MB\u001b[0m \u001b[31m72.6 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m:00:01\u001b[0m00:01\u001b[0m\n\u001b[?25hInstalling collected packages: scikit-learn\n  Attempting uninstall: scikit-learn\n    Found existing installation: scikit-learn 1.2.2\n    Uninstalling scikit-learn-1.2.2:\n      Successfully uninstalled scikit-learn-1.2.2\n\u001b[31mERROR: pip's dependency resolver does not currently take into account all the packages that are installed. This behaviour is the source of the following dependency conflicts.\nspopt 0.6.0 requires shapely>=2.0.1, but you have shapely 1.8.5.post1 which is incompatible.\u001b[0m\u001b[31m\n\u001b[0mSuccessfully installed scikit-learn-1.4.1.post1\n","output_type":"stream"}]},{"cell_type":"markdown","source":"# BELL CURVE DATA","metadata":{"_uuid":"78b449ba-833f-4748-9c86-c2a5ce5870a2","_cell_guid":"28346c8d-e121-4019-ba61-c9394c903f24","trusted":true}},{"cell_type":"code","source":"import pandas as pd\nimport numpy as np\n\n# Define the mean and standard deviation for each column\nlevel_mean = 300\nlevel_std = 50\ncourse_units_mean = 2\ncourse_units_std = 0.5\nattendance_mean = 5\nattendance_std = 2\nmid_semester_mean = 10\nmid_semester_std = 3\nassignments_mean = 5\nassignments_std = 2\nexam_mean = 30\nexam_std = 10\n\n# Define the covariance matrix\ncov_matrix = np.array([\n    [level_std**2, 0, 0, 0, 0, 0],\n    [0, course_units_std**2, 0, 0, 0, 0],\n    [0, 0, attendance_std**2, 0, 0, 0.9 * attendance_std * exam_std],\n    [0, 0, 0, mid_semester_std**2, 0, 0.7 * mid_semester_std * exam_std],\n    [0, 0, 0, 0, assignments_std**2, 0.5 * assignments_std * exam_std],\n    [0, 0, 0.9 * attendance_std * exam_std, 0.7 * mid_semester_std * exam_std, 0.5 * assignments_std * exam_std, exam_std**2]\n])\n\n# Generate correlated random variables\ncorrelated_vars = np.random.multivariate_normal(\n    [level_mean, course_units_mean, attendance_mean, mid_semester_mean, assignments_mean, exam_mean],\n    cov_matrix,\n    500000\n)\n\n# Extract the individual variables\nlevel = np.round(correlated_vars[:, 0] / 100) * 100  # Round to the nearest 100\ncourse_units = correlated_vars[:, 1].astype(int)\nattendance = correlated_vars[:, 2].astype(int)\nmid_semester = correlated_vars[:, 3].astype(int)\nassignments = correlated_vars[:, 4].astype(int)\nexam = correlated_vars[:, 5].astype(int)\n\n# Clip the values to the desired range\nlevel = np.clip(level, 200, 400)\ncourse_units = np.clip(course_units, 1, 3)\nattendance = np.clip(attendance, 1, 10)\nmid_semester = np.clip(mid_semester, 3, 20)\nassignments = np.clip(assignments, 2, 10)\nexam = np.clip(exam, 15, 60)\n\n# Create a DataFrame with the generated data\ndata = {\n    'Level': level,\n    'Course Units': course_units,\n    'Attendance': attendance,\n    'Mid Semester': mid_semester,\n    'Assignments': assignments,\n    'Exam': exam\n}\n\ndf = pd.DataFrame(data)\n\n# Save the generated dataset to a CSV file\ndf.to_csv('generated_dataset.csv', index=False)","metadata":{"_uuid":"229b50eb-5d41-411a-9d6d-c83f24015790","_cell_guid":"ee2d6ae8-a49e-415a-817d-044d97438b25","collapsed":false,"jupyter":{"outputs_hidden":false},"execution":{"iopub.status.busy":"2024-04-05T19:38:17.181102Z","iopub.execute_input":"2024-04-05T19:38:17.181543Z","iopub.status.idle":"2024-04-05T19:38:18.456526Z","shell.execute_reply.started":"2024-04-05T19:38:17.181508Z","shell.execute_reply":"2024-04-05T19:38:18.455090Z"},"trusted":true},"execution_count":34,"outputs":[{"name":"stderr","text":"/tmp/ipykernel_33/3474468994.py:29: RuntimeWarning: covariance is not symmetric positive-semidefinite.\n  correlated_vars = np.random.multivariate_normal(\n","output_type":"stream"}]},{"cell_type":"markdown","source":"# CREATING CORRELATION","metadata":{"_uuid":"bb22047a-f98b-48af-9e08-7ad05f5a8e75","_cell_guid":"6e35a962-3c25-4989-aaff-f90f1361067c","trusted":true}},{"cell_type":"code","source":"# import pandas as pd\n# import numpy as np\n\n# # Define the mean and standard deviation for each column\n# level_mean = 300\n# level_std = 50\n# course_units_mean = 2\n# course_units_std = 0.5\n# attendance_mean = 5\n# attendance_std = 2\n# mid_semester_mean = 10\n# mid_semester_std = 3\n# assignments_mean = 5\n# assignments_std = 2\n# exam_mean = 30\n# exam_std = 10\n\n# # Define the covariance matrix\n# cov_matrix = np.array([\n#     [level_std**2, 0, 0, 0, 0, 0],\n#     [0, course_units_std**2, 0, 0, 0, -0.3 * course_units_std * exam_std],\n#     [0, 0, attendance_std**2, 0, 0.5 * attendance_std * exam_std, 0.5 * attendance_std * exam_std],\n#     [0, 0, 0, mid_semester_std**2, 0.4 * mid_semester_std * exam_std, 0.4 * mid_semester_std * exam_std],\n#     [0, 0, 0.5 * attendance_std * exam_std, 0.4 * mid_semester_std * exam_std, assignments_std**2, 0.7 * assignments_std * exam_std],\n#     [0, -0.3 * course_units_std * exam_std, 0.5 * attendance_std * exam_std, 0.4 * mid_semester_std * exam_std, 0.7 * assignments_std * exam_std, exam_std**2]\n# ])\n\n# # Generate correlated random variables\n# correlated_vars = np.random.multivariate_normal(\n#     [level_mean, course_units_mean, attendance_mean, mid_semester_mean, assignments_mean, exam_mean],\n#     cov_matrix,\n#     500000\n# )\n\n# # Extract the individual variables\n# level = np.round(correlated_vars[:, 0] / 100) * 100  # Round to the nearest 100\n# course_units = correlated_vars[:, 1].astype(int)\n# attendance = correlated_vars[:, 2].astype(int)\n# mid_semester = correlated_vars[:, 3].astype(int)\n# assignments = correlated_vars[:, 4].astype(int)\n# exam = correlated_vars[:, 5].astype(int)\n\n# # Generate random gender values\n# gender = np.random.choice([0, 1], size=500000)\n\n# # Clip the values to the desired range\n# level = np.clip(level, 200, 400)\n# course_units = np.clip(course_units, 1, 3)\n# attendance = np.clip(attendance, 1, 10)\n# mid_semester = np.clip(mid_semester, 1, 20)\n# assignments = np.clip(assignments, 1, 10)\n# exam = np.clip(exam, 1, 60)\n\n# # Create a DataFrame with the generated data\n# data = {\n#     'Level': level,\n#     'Course Units': course_units,\n#     'Attendance': attendance,\n#     'Mid Semester': mid_semester,\n#     'Assignments': assignments,\n#     'Exam': exam,\n#     'Gender': gender\n# }\n\n# df = pd.DataFrame(data)\n\n# # Save the generated dataset to a CSV file\n# df.to_csv('generated_dataset.csv', index=False)","metadata":{"_uuid":"e712bed9-3e6d-4fb5-b64e-e320764bec7c","_cell_guid":"6aa0b6e5-3083-404e-899e-edff60a825a8","collapsed":false,"jupyter":{"outputs_hidden":false},"execution":{"iopub.status.busy":"2024-04-05T19:38:18.460527Z","iopub.execute_input":"2024-04-05T19:38:18.461088Z","iopub.status.idle":"2024-04-05T19:38:18.468983Z","shell.execute_reply.started":"2024-04-05T19:38:18.461037Z","shell.execute_reply":"2024-04-05T19:38:18.467583Z"},"trusted":true},"execution_count":35,"outputs":[]},{"cell_type":"code","source":"level = np.round(correlated_vars[:, 0] / 100) * 100  # Round to the nearest 100","metadata":{"_uuid":"5229e9e5-4be2-4237-a6d2-15b72667cd51","_cell_guid":"40206c97-66aa-4de6-8ea0-ab195b86ded9","collapsed":false,"jupyter":{"outputs_hidden":false},"execution":{"iopub.status.busy":"2024-04-05T19:38:18.470698Z","iopub.execute_input":"2024-04-05T19:38:18.471450Z","iopub.status.idle":"2024-04-05T19:38:18.488706Z","shell.execute_reply.started":"2024-04-05T19:38:18.471413Z","shell.execute_reply":"2024-04-05T19:38:18.487331Z"},"trusted":true},"execution_count":36,"outputs":[]},{"cell_type":"code","source":"df","metadata":{"_uuid":"3ce1f5b5-56f8-4293-87c2-b68ef6ec43d8","_cell_guid":"50c4328d-af03-4fa8-9e9b-19564ce1a7c7","collapsed":false,"jupyter":{"outputs_hidden":false},"execution":{"iopub.status.busy":"2024-04-05T19:38:18.490206Z","iopub.execute_input":"2024-04-05T19:38:18.490582Z","iopub.status.idle":"2024-04-05T19:38:18.507944Z","shell.execute_reply.started":"2024-04-05T19:38:18.490550Z","shell.execute_reply":"2024-04-05T19:38:18.506562Z"},"trusted":true},"execution_count":37,"outputs":[{"execution_count":37,"output_type":"execute_result","data":{"text/plain":"        Level  Course Units  Attendance  Mid Semester  Assignments  Exam\n0       400.0             1           8             7            9    35\n1       300.0             1           1             3            2    15\n2       300.0             1           5             9            3    21\n3       300.0             1           6             7            5    25\n4       400.0             2           1             8            4    15\n...       ...           ...         ...           ...          ...   ...\n499995  200.0             2           4            10            5    28\n499996  300.0             2           7            16            8    40\n499997  300.0             2           3            11            6    34\n499998  400.0             1           4             5            3    35\n499999  300.0             2           6            14            9    42\n\n[500000 rows x 6 columns]","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>Level</th>\n      <th>Course Units</th>\n      <th>Attendance</th>\n      <th>Mid Semester</th>\n      <th>Assignments</th>\n      <th>Exam</th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th>0</th>\n      <td>400.0</td>\n      <td>1</td>\n      <td>8</td>\n      <td>7</td>\n      <td>9</td>\n      <td>35</td>\n    </tr>\n    <tr>\n      <th>1</th>\n      <td>300.0</td>\n      <td>1</td>\n      <td>1</td>\n      <td>3</td>\n      <td>2</td>\n      <td>15</td>\n    </tr>\n    <tr>\n      <th>2</th>\n      <td>300.0</td>\n      <td>1</td>\n      <td>5</td>\n      <td>9</td>\n      <td>3</td>\n      <td>21</td>\n    </tr>\n    <tr>\n      <th>3</th>\n      <td>300.0</td>\n      <td>1</td>\n      <td>6</td>\n      <td>7</td>\n      <td>5</td>\n      <td>25</td>\n    </tr>\n    <tr>\n      <th>4</th>\n      <td>400.0</td>\n      <td>2</td>\n      <td>1</td>\n      <td>8</td>\n      <td>4</td>\n      <td>15</td>\n    </tr>\n    <tr>\n      <th>...</th>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n    </tr>\n    <tr>\n      <th>499995</th>\n      <td>200.0</td>\n      <td>2</td>\n      <td>4</td>\n      <td>10</td>\n      <td>5</td>\n      <td>28</td>\n    </tr>\n    <tr>\n      <th>499996</th>\n      <td>300.0</td>\n      <td>2</td>\n      <td>7</td>\n      <td>16</td>\n      <td>8</td>\n      <td>40</td>\n    </tr>\n    <tr>\n      <th>499997</th>\n      <td>300.0</td>\n      <td>2</td>\n      <td>3</td>\n      <td>11</td>\n      <td>6</td>\n      <td>34</td>\n    </tr>\n    <tr>\n      <th>499998</th>\n      <td>400.0</td>\n      <td>1</td>\n      <td>4</td>\n      <td>5</td>\n      <td>3</td>\n      <td>35</td>\n    </tr>\n    <tr>\n      <th>499999</th>\n      <td>300.0</td>\n      <td>2</td>\n      <td>6</td>\n      <td>14</td>\n      <td>9</td>\n      <td>42</td>\n    </tr>\n  </tbody>\n</table>\n<p>500000 rows × 6 columns</p>\n</div>"},"metadata":{}}]},{"cell_type":"code","source":"df['Total'] = df['Attendance'] + df['Mid Semester'] + df['Assignments'] + df['Exam']","metadata":{"_uuid":"f9caad4a-98fc-4966-b4e4-89e889d4aabc","_cell_guid":"57007f3e-89d6-4348-a940-0c74d37fb986","collapsed":false,"jupyter":{"outputs_hidden":false},"execution":{"iopub.status.busy":"2024-04-05T19:38:18.509972Z","iopub.execute_input":"2024-04-05T19:38:18.510422Z","iopub.status.idle":"2024-04-05T19:38:18.523581Z","shell.execute_reply.started":"2024-04-05T19:38:18.510386Z","shell.execute_reply":"2024-04-05T19:38:18.522130Z"},"trusted":true},"execution_count":38,"outputs":[]},{"cell_type":"code","source":"df.head()","metadata":{"_uuid":"3f709192-ece2-4631-9720-bd17391d6fdb","_cell_guid":"2b3e726e-b8be-4611-a7a7-91f60f109648","collapsed":false,"jupyter":{"outputs_hidden":false},"execution":{"iopub.status.busy":"2024-04-05T19:38:18.525560Z","iopub.execute_input":"2024-04-05T19:38:18.526003Z","iopub.status.idle":"2024-04-05T19:38:18.538655Z","shell.execute_reply.started":"2024-04-05T19:38:18.525968Z","shell.execute_reply":"2024-04-05T19:38:18.537607Z"},"trusted":true},"execution_count":39,"outputs":[{"execution_count":39,"output_type":"execute_result","data":{"text/plain":"   Level  Course Units  Attendance  Mid Semester  Assignments  Exam  Total\n0  400.0             1           8             7            9    35     59\n1  300.0             1           1             3            2    15     21\n2  300.0             1           5             9            3    21     38\n3  300.0             1           6             7            5    25     43\n4  400.0             2           1             8            4    15     28","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>Level</th>\n      <th>Course Units</th>\n      <th>Attendance</th>\n      <th>Mid Semester</th>\n      <th>Assignments</th>\n      <th>Exam</th>\n      <th>Total</th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th>0</th>\n      <td>400.0</td>\n      <td>1</td>\n      <td>8</td>\n      <td>7</td>\n      <td>9</td>\n      <td>35</td>\n      <td>59</td>\n    </tr>\n    <tr>\n      <th>1</th>\n      <td>300.0</td>\n      <td>1</td>\n      <td>1</td>\n      <td>3</td>\n      <td>2</td>\n      <td>15</td>\n      <td>21</td>\n    </tr>\n    <tr>\n      <th>2</th>\n      <td>300.0</td>\n      <td>1</td>\n      <td>5</td>\n      <td>9</td>\n      <td>3</td>\n      <td>21</td>\n      <td>38</td>\n    </tr>\n    <tr>\n      <th>3</th>\n      <td>300.0</td>\n      <td>1</td>\n      <td>6</td>\n      <td>7</td>\n      <td>5</td>\n      <td>25</td>\n      <td>43</td>\n    </tr>\n    <tr>\n      <th>4</th>\n      <td>400.0</td>\n      <td>2</td>\n      <td>1</td>\n      <td>8</td>\n      <td>4</td>\n      <td>15</td>\n      <td>28</td>\n    </tr>\n  </tbody>\n</table>\n</div>"},"metadata":{}}]},{"cell_type":"code","source":"import seaborn as sns\nimport matplotlib.pyplot as plt\n\nsns.histplot(data=df, x='Level', kde=True)\nplt.title('Level Distribution Plot')\nplt.show()","metadata":{"_uuid":"2af00047-168b-4200-b269-bf177fac248c","_cell_guid":"b3c3a1cb-37bf-41ba-8c82-fb9cc2698fbf","collapsed":false,"jupyter":{"outputs_hidden":false},"execution":{"iopub.status.busy":"2024-04-05T19:38:18.539961Z","iopub.execute_input":"2024-04-05T19:38:18.540374Z","iopub.status.idle":"2024-04-05T19:38:20.647313Z","shell.execute_reply.started":"2024-04-05T19:38:18.540342Z","shell.execute_reply":"2024-04-05T19:38:20.646112Z"},"trusted":true},"execution_count":40,"outputs":[{"name":"stderr","text":"/opt/conda/lib/python3.10/site-packages/seaborn/_oldcore.py:1119: FutureWarning: use_inf_as_na option is deprecated and will be removed in a future version. Convert inf values to NaN before operating instead.\n  with pd.option_context('mode.use_inf_as_na', True):\n","output_type":"stream"},{"output_type":"display_data","data":{"text/plain":"<Figure size 640x480 with 1 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"},"metadata":{}}]},{"cell_type":"code","source":"sns.histplot(data=df, x='Course Units', kde=True)\nplt.title('Course Units Distribution Plot')\nplt.show()","metadata":{"_uuid":"725f9dcd-d093-46d2-93c1-eb4ce2816b0a","_cell_guid":"140d69a4-6dc1-42d4-9bce-34ff484750af","collapsed":false,"jupyter":{"outputs_hidden":false},"execution":{"iopub.status.busy":"2024-04-05T19:38:20.652091Z","iopub.execute_input":"2024-04-05T19:38:20.653166Z","iopub.status.idle":"2024-04-05T19:38:23.006230Z","shell.execute_reply.started":"2024-04-05T19:38:20.653119Z","shell.execute_reply":"2024-04-05T19:38:23.004936Z"},"trusted":true},"execution_count":41,"outputs":[{"name":"stderr","text":"/opt/conda/lib/python3.10/site-packages/seaborn/_oldcore.py:1119: FutureWarning: use_inf_as_na option is deprecated and will be removed in a future version. Convert inf values to NaN before operating instead.\n  with pd.option_context('mode.use_inf_as_na', True):\n","output_type":"stream"},{"output_type":"display_data","data":{"text/plain":"<Figure size 640x480 with 1 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"},"metadata":{}}]},{"cell_type":"code","source":"sns.histplot(data=df, x='Attendance', kde=True)\nplt.title('Attendance Distribution Plot')\nplt.show()","metadata":{"_uuid":"3cd93a46-3525-4fe2-a3a9-d6e1922319b8","_cell_guid":"271f1363-5ec7-4f60-a460-936a1c5a3e2a","collapsed":false,"jupyter":{"outputs_hidden":false},"execution":{"iopub.status.busy":"2024-04-05T19:38:23.007440Z","iopub.execute_input":"2024-04-05T19:38:23.007765Z","iopub.status.idle":"2024-04-05T19:38:25.328378Z","shell.execute_reply.started":"2024-04-05T19:38:23.007736Z","shell.execute_reply":"2024-04-05T19:38:25.326975Z"},"trusted":true},"execution_count":42,"outputs":[{"name":"stderr","text":"/opt/conda/lib/python3.10/site-packages/seaborn/_oldcore.py:1119: FutureWarning: use_inf_as_na option is deprecated and will be removed in a future version. Convert inf values to NaN before operating instead.\n  with pd.option_context('mode.use_inf_as_na', True):\n","output_type":"stream"},{"output_type":"display_data","data":{"text/plain":"<Figure size 640x480 with 1 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"},"metadata":{}}]},{"cell_type":"code","source":"sns.histplot(data=df, x='Mid Semester', kde=True)\nplt.title('Mid Semester Distribution Plot')\nplt.show()","metadata":{"_uuid":"143749c6-3ef0-417a-8301-a765b79174f5","_cell_guid":"e63ede7a-e59d-4120-9802-675897017644","collapsed":false,"jupyter":{"outputs_hidden":false},"execution":{"iopub.status.busy":"2024-04-05T19:38:25.329732Z","iopub.execute_input":"2024-04-05T19:38:25.330086Z","iopub.status.idle":"2024-04-05T19:38:27.850941Z","shell.execute_reply.started":"2024-04-05T19:38:25.330055Z","shell.execute_reply":"2024-04-05T19:38:27.849615Z"},"trusted":true},"execution_count":43,"outputs":[{"name":"stderr","text":"/opt/conda/lib/python3.10/site-packages/seaborn/_oldcore.py:1119: FutureWarning: use_inf_as_na option is deprecated and will be removed in a future version. Convert inf values to NaN before operating instead.\n  with pd.option_context('mode.use_inf_as_na', True):\n","output_type":"stream"},{"output_type":"display_data","data":{"text/plain":"<Figure size 640x480 with 1 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"},"metadata":{}}]},{"cell_type":"code","source":"sns.histplot(data=df, x='Assignments', kde=True)\nplt.title('Assignments Distribution Plot')\nplt.show()","metadata":{"_uuid":"5d642d9e-0c7c-4eec-a4fd-c9f11945723e","_cell_guid":"5493c2c3-0159-4e56-a024-3747560996d5","collapsed":false,"jupyter":{"outputs_hidden":false},"execution":{"iopub.status.busy":"2024-04-05T19:38:27.852427Z","iopub.execute_input":"2024-04-05T19:38:27.853002Z","iopub.status.idle":"2024-04-05T19:38:30.215642Z","shell.execute_reply.started":"2024-04-05T19:38:27.852963Z","shell.execute_reply":"2024-04-05T19:38:30.214461Z"},"trusted":true},"execution_count":44,"outputs":[{"name":"stderr","text":"/opt/conda/lib/python3.10/site-packages/seaborn/_oldcore.py:1119: FutureWarning: use_inf_as_na option is deprecated and will be removed in a future version. Convert inf values to NaN before operating instead.\n  with pd.option_context('mode.use_inf_as_na', True):\n","output_type":"stream"},{"output_type":"display_data","data":{"text/plain":"<Figure size 640x480 with 1 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"},"metadata":{}}]},{"cell_type":"code","source":"sns.histplot(data=df, x='Exam', kde=True)\nplt.title('Exam Distribution Plot')\nplt.show()","metadata":{"_uuid":"a2d714f9-0c22-44cf-bf8b-4268dc22ae7d","_cell_guid":"e56d031d-733c-4d84-a18a-25b80abdd32e","collapsed":false,"jupyter":{"outputs_hidden":false},"execution":{"iopub.status.busy":"2024-04-05T19:38:30.217222Z","iopub.execute_input":"2024-04-05T19:38:30.218287Z","iopub.status.idle":"2024-04-05T19:38:32.699315Z","shell.execute_reply.started":"2024-04-05T19:38:30.218251Z","shell.execute_reply":"2024-04-05T19:38:32.698114Z"},"trusted":true},"execution_count":45,"outputs":[{"name":"stderr","text":"/opt/conda/lib/python3.10/site-packages/seaborn/_oldcore.py:1119: FutureWarning: use_inf_as_na option is deprecated and will be removed in a future version. Convert inf values to NaN before operating instead.\n  with pd.option_context('mode.use_inf_as_na', True):\n","output_type":"stream"},{"output_type":"display_data","data":{"text/plain":"<Figure size 640x480 with 1 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"},"metadata":{}}]},{"cell_type":"code","source":"import pandas as pd\nfrom sklearn.model_selection import train_test_split\nfrom sklearn.ensemble import RandomForestRegressor, GradientBoostingRegressor\nfrom sklearn.linear_model import LinearRegression\nfrom sklearn.metrics import mean_squared_error, r2_score, mean_absolute_error, accuracy_score\n\n# Assuming you have a DataFrame 'df' with relevant columns\n\n# Separate features and target variables\nX = df[['Level', 'Course Units', 'Attendance', 'Mid Semester', 'Assignments']]\ny = df['Exam']\n\n# Split the data into training and testing sets\nX_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=42)\n\n# Initialize models\nmodels = [\n    RandomForestRegressor(),\n    GradientBoostingRegressor(),\n    LinearRegression()\n]\n\n# Fit and evaluate models\nfor model in models:\n    model.fit(X_train, y_train)\n    y_pred = model.predict(X_test)\n\n    print(f\"{model.__class__.__name__} Model\")\n    print(f\"Mean Squared Error: {mean_squared_error(y_test, y_pred):.2f}\")\n    print(f\"R-squared: {r2_score(y_test, y_pred):.2f}\")\n    print(f\"Mean Absolute Error: {mean_absolute_error(y_test, y_pred):.2f}\")\n    print(f\"Accuracy: {accuracy_score(y_test, [round(y) for y in y_pred]):.2f}\")\n    print()","metadata":{"_uuid":"df8eb974-bf74-4b52-a139-49db7b458c00","_cell_guid":"ee39b693-1eb2-40aa-a823-f893dab6638e","collapsed":false,"jupyter":{"outputs_hidden":false},"execution":{"iopub.status.busy":"2024-04-05T19:38:32.700781Z","iopub.execute_input":"2024-04-05T19:38:32.701167Z"},"trusted":true},"execution_count":null,"outputs":[{"name":"stdout","text":"RandomForestRegressor Model\nMean Squared Error: 25.97\nR-squared: 0.70\nMean Absolute Error: 4.04\nAccuracy: 0.08\n\n","output_type":"stream"}]},{"cell_type":"code","source":"# # Predict 'Exam' scores\n# rf_exam_pred = rf_model.predict(X_test)\n# gb_exam_pred = gb_model.predict(X_test)\n# lr_exam_pred = lr_model.predict(X_test)\n\n# # Evaluate models for 'Exam' prediction\n# rf_exam_mse = mean_squared_error(y_exam_test, rf_exam_pred)\n# gb_exam_mse = mean_squared_error(y_exam_test, gb_exam_pred)\n# lr_exam_mse = mean_squared_error(y_exam_test, lr_exam_pred)\n\n# # Additional regression metrics\n# rf_r2 = r2_score(y_exam_test, rf_exam_pred)\n# gb_r2 = r2_score(y_exam_test, gb_exam_pred)\n# lr_r2 = r2_score(y_exam_test, lr_exam_pred)\n\n# rf_mae = mean_absolute_error(y_exam_test, rf_exam_pred)\n# gb_mae = mean_absolute_error(y_exam_test, gb_exam_pred)\n# lr_mae = mean_absolute_error(y_exam_test, lr_exam_pred)\n\n# # Calculate accuracy as 1 - (MSE/variance)\n# rf_accuracy = 1 - (rf_exam_mse / y_exam_test.var())\n# gb_accuracy = 1 - (gb_exam_mse / y_exam_test.var())\n# lr_accuracy = 1 - (lr_exam_mse / y_exam_test.var())","metadata":{"_uuid":"03acdc46-d6ed-47b8-acba-5ddad5207794","_cell_guid":"d56a6ea7-39b1-4b15-af6a-2fc4983514c7","collapsed":false,"jupyter":{"outputs_hidden":false},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"\n# print(\"Random Forest Model - Exam MSE:\", rf_exam_mse)\n# print(\"Gradient Boosting Model - Exam MSE:\", gb_exam_mse)\n# print(\"Linear Regression Model - Exam MSE:\", lr_exam_mse)\n\n# print(\"Random Forest Model - R-squared:\", rf_r2)\n# print(\"Gradient Boosting Model - R-squared:\", gb_r2)\n# print(\"Linear Regression Model - R-squared:\", lr_r2)\n\n# print(\"Random Forest Model - MAE:\", rf_mae)\n# print(\"Gradient Boosting Model - MAE:\", gb_mae)\n# print(\"Linear Regression Model - MAE:\", lr_mae)\n\n# print(\"Random Forest Model - Accuracy:\", rf_accuracy)\n# print(\"Gradient Boosting Model - Accuracy:\", gb_accuracy)\n# print(\"Linear Regression Model - Accuracy:\", lr_accuracy)","metadata":{"_uuid":"61d9ee81-c723-4482-9db6-844f251b33b8","_cell_guid":"b0613d09-4439-4889-8d48-f3aef6b5f745","collapsed":false,"jupyter":{"outputs_hidden":false},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"import pandas as pd\nimport numpy as np\nfrom sklearn.model_selection import train_test_split\nfrom sklearn.preprocessing import StandardScaler\nimport tensorflow as tf\nfrom tensorflow import keras\nfrom tensorflow.keras import layers\n\n\n# Standardize the features\nscaler = StandardScaler()\nX_train_scaled = scaler.fit_transform(X_train)\nX_test_scaled = scaler.transform(X_test)\n\n# Define the neural network model\nmodel = keras.Sequential([\n    layers.Dense(64, activation='relu', input_shape=[X_train_scaled.shape[1]]),\n    layers.Dense(32, activation='relu'),\n    layers.Dense(1)\n])\n\n# Compile the model\nmodel.compile(optimizer='adam',\n              loss='mse',\n              metrics=['mae'])","metadata":{"_uuid":"407165fe-ba95-44b0-8c45-5e5a649a7c2a","_cell_guid":"6de043be-dc78-475e-826b-d8de149f5113","collapsed":false,"jupyter":{"outputs_hidden":false},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"model.summary()","metadata":{"_uuid":"e2ff5040-5291-413f-b2c3-667f24c983f3","_cell_guid":"2d4947d0-9948-4917-b1d3-73b06cd79c1f","collapsed":false,"jupyter":{"outputs_hidden":false},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"# Train the model\nepochs = 10\nbatch_size = 32\nhistory = model.fit(X_train_scaled, y_train, epochs=epochs, batch_size=batch_size, validation_split=0.2, verbose=1, shuffle=True)\n\n# Evaluate the model on the test set\ntest_loss, test_mae = model.evaluate(X_test_scaled, y_test, verbose=0)\nprint(f'Test Loss: {test_loss:.4f}')\nprint(f'Test MAE: {test_mae:.4f}')","metadata":{"_uuid":"f02f9ef8-0af5-4658-b0dd-e5077081a68a","_cell_guid":"8581562f-c265-431e-8346-2641db75b687","collapsed":false,"jupyter":{"outputs_hidden":false},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"# Split the data into features and target\nX = df[['Level', 'Course Units', 'Attendance', 'Mid Semester', 'Assignments']]\ny = df['Exam']\n\n# Initialize models\nrf_model = RandomForestRegressor()\ngb_model = GradientBoostingRegressor()\nlr_model = LinearRegression()\n\n# Fit the models\nrf_model.fit(X, y)\ngb_model.fit(X, y)\nlr_model.fit(X, y)","metadata":{"_uuid":"f2f7890d-90b8-4ef1-8ff4-968054d92d9b","_cell_guid":"5f6b59a0-8ade-4a7d-9b81-9e7ac2af39b0","collapsed":false,"jupyter":{"outputs_hidden":false},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"def predict_exam_score(model, level, course_units, attendance, mid_semester, assignments):\n    \"\"\"\n    Predict the 'Exam' score based on the input features using the specified model.\n\n    Args:\n        model (str): The model to use for prediction ('rf', 'gb', or 'lr').\n        level (int): The level (200, 300, or 400).\n        course_units (int): The number of course units (1, 2, or 3).\n        attendance (int): The attendance score (1-10).\n        mid_semester (int): The mid-semester score (3-20).\n        assignments (int): The assignments score (2-10).\n\n    Returns:\n        float: The predicted 'Exam' score.\n    \"\"\"\n    if model == 'rf':\n        selected_model = rf_model\n    elif model == 'gb':\n        selected_model = gb_model\n    elif model == 'lr':\n        selected_model = lr_model\n    else:\n        raise ValueError(\"Invalid model specified. Choose 'rf', 'gb', or 'lr'.\")\n\n    input_data = pd.DataFrame({\n        'Level': [level],\n        'Course Units': [course_units],\n        'Attendance': [attendance],\n        'Mid Semester': [mid_semester],\n        'Assignments': [assignments]\n    })\n\n    predicted_score = selected_model.predict(input_data)\n    return predicted_score[0]","metadata":{"_uuid":"0486131b-a1df-4ff5-93e9-2780c6f8b3a4","_cell_guid":"efecdac6-22fc-49fd-abc4-dedf268f4d96","collapsed":false,"jupyter":{"outputs_hidden":false},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"# Example usage\nlevel = 400\ncourse_units = 3\nattendance = 0\nmid_semester = 10\nassignments = 10\n\nrf_prediction = predict_exam_score('rf', level, course_units, attendance, mid_semester, assignments)\nprint(f\"Random Forest Prediction: {rf_prediction:.2f}\")\n\ngb_prediction = predict_exam_score('gb', level, course_units, attendance, mid_semester, assignments)\nprint(f\"Gradient Boosting Prediction: {gb_prediction:.2f}\")\n\nlr_prediction = predict_exam_score('lr', level, course_units, attendance, mid_semester, assignments)\nprint(f\"Linear Regression Prediction: {lr_prediction:.2f}\")","metadata":{"_uuid":"0ace2c71-3791-4e6b-8b54-a79d2353b6fd","_cell_guid":"a4307e2d-2446-4a56-afab-61eb690d996e","collapsed":false,"jupyter":{"outputs_hidden":false},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"import pickle\n\nwith open('gb1_model.sav', 'wb') as file:\n    pickle.dump(gb_model, file)","metadata":{"_uuid":"6ae71f09-4d8a-45e1-bad6-49c54b762829","_cell_guid":"00658713-5de6-49b2-9c2a-1fcf68c6029e","collapsed":false,"jupyter":{"outputs_hidden":false},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"import sklearn\nprint(sklearn.__version__)","metadata":{"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"","metadata":{},"execution_count":null,"outputs":[]}]}