From af6f60f048781cf675bce0576620e1f0ee61b587 Mon Sep 17 00:00:00 2001 From: Sang Putu Sandhyana Yogi <79888425+youronlydimwit@users.noreply.github.com> Date: Tue, 16 Jul 2024 18:00:42 +0700 Subject: [PATCH] Delete EDA_Workflow/Untitled1.ipynb --- EDA_Workflow/Untitled1.ipynb | 18518 --------------------------------- 1 file changed, 18518 deletions(-) delete mode 100644 EDA_Workflow/Untitled1.ipynb diff --git a/EDA_Workflow/Untitled1.ipynb b/EDA_Workflow/Untitled1.ipynb deleted file mode 100644 index a63064f..0000000 --- a/EDA_Workflow/Untitled1.ipynb +++ /dev/null @@ -1,18518 +0,0 @@ -{ - "cells": [ - { - "cell_type": "code", - "execution_count": 1, - "id": "0f533d0f", - "metadata": {}, - "outputs": [], - "source": [ - "import numpy as np\n", - "import pandas as pd\n", - "from ydata_profiling import ProfileReport" - ] - }, - { - "cell_type": "code", - "execution_count": 5, - "id": "0b0c3d0d", - "metadata": {}, - "outputs": [ - { - "data": { - "text/html": [ - "
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\n", 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" - ] - }, - "metadata": { - "needs_background": "light" - }, - "output_type": "display_data" - } - ], - "source": [ - "isnumeric_boxplots(df)" - ] - }, - { - "cell_type": "code", - "execution_count": 17, - "id": "03b8e74f", - "metadata": {}, - "outputs": [], - "source": [ - "from sklearn.preprocessing import StandardScaler" - ] - }, - { - "cell_type": "code", - "execution_count": 18, - "id": "3d46540f", - "metadata": {}, - "outputs": [], - "source": [ - "def scale_dataframe(df):\n", - " # Identify numeric columns\n", - " numeric_columns = df.select_dtypes(include=['number']).columns\n", - "\n", - " # Create a copy of the DataFrame to avoid modifying the original\n", - " scaled_df = df.copy()\n", - "\n", - " # Apply StandardScaler to numeric columns\n", - " scaler = StandardScaler()\n", - " scaled_df[numeric_columns] = scaler.fit_transform(scaled_df[numeric_columns])\n", - "\n", - " return scaled_df" - ] - }, - { - "cell_type": "code", - "execution_count": 22, - "id": "c1ba186a", - "metadata": {}, - "outputs": [], - "source": [ - "df_scaled = df.copy()" - ] - }, - { - "cell_type": "code", - "execution_count": 23, - "id": "e1169507", - "metadata": {}, - "outputs": [ - { - "data": { - "text/html": [ - "
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} - ], - "source": [ - "scale_dataframe(df_scaled)" - ] - }, - { - "cell_type": "code", - "execution_count": 24, - "id": "810853d6", - "metadata": {}, - "outputs": [], - "source": [ - "from sklearn.model_selection import train_test_split\n", - "from sklearn.model_selection import cross_val_score\n", - "from sklearn.metrics import accuracy_score\n", - "from sklearn.ensemble import RandomForestClassifier # You can replace this with the model of your choice" - ] - }, - { - "cell_type": "code", - "execution_count": 25, - "id": "088336e5", - "metadata": {}, - "outputs": [], - "source": [ - "def run_prediction_model(df):\n", - " # Assuming the last column is the label\n", - " X = df.iloc[:, :-1] # Features\n", - " y = df.iloc[:, -1] # Label\n", - "\n", - " # Split the data into training and testing sets\n", - " X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=42)\n", - "\n", - " # Initialize your model (replace RandomForestClassifier with the model of your choice)\n", - " model = RandomForestClassifier()\n", - "\n", - " # Train the model\n", - " model.fit(X_train, y_train)\n", - "\n", - " # Make predictions on the test set\n", - " y_pred = model.predict(X_test)\n", - "\n", - " # Evaluate the model (you can customize this based on your problem)\n", - " accuracy = accuracy_score(y_test, y_pred)\n", - " print(f'Accuracy: {accuracy}')\n", - "\n", - " # Optionally, you can perform cross-validation\n", - " cross_val_scores = cross_val_score(model, X, y, cv=5) # 5-fold cross-validation\n", - " print(f'Cross-Validation Scores: {cross_val_scores}')\n", - " print(f'Mean Cross-Validation Score: {cross_val_scores.mean()}')" - ] - }, - { - "cell_type": "code", - "execution_count": 26, - "id": "0d672f97", - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Accuracy: 0.9202020202020202\n", - "Cross-Validation Scores: [0.92070707 0.92373737 0.92929293 0.9201617 0.91460334]\n", - "Mean Cross-Validation Score: 0.921700481316449\n" - ] - } - ], - "source": [ - "run_prediction_model(df)" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "cf72229c", - "metadata": {}, - "outputs": [], - "source": [ - "# Predict with a sample df" - ] - }, - { - "cell_type": "code", - "execution_count": 27, - "id": "84b3eece", - "metadata": {}, - "outputs": [], - "source": [ - "from sklearn.ensemble import RandomForestClassifier " - ] - }, - { - "cell_type": "code", - "execution_count": 28, - "id": "d982dc6b", - "metadata": {}, - "outputs": [], - "source": [ - "def run_multi_column_prediction_model(df):\n", - " # Initialize your model (replace RandomForestClassifier with the model of your choice)\n", - " model = RandomForestClassifier()\n", - "\n", - " for column in df.columns[:-1]: # Exclude the last column, assumed to be the label\n", - " print(f'\\nTraining and evaluating model for predicting: {column}')\n", - "\n", - " # Features and label\n", - " X = df.drop(column, axis=1) # Features\n", - " y = df[column] # Label\n", - "\n", - " # Split the data into training and testing sets\n", - " X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=42)\n", - "\n", - " # Train the model\n", - " model.fit(X_train, y_train)\n", - "\n", - " # Make predictions on the test set\n", - " y_pred = model.predict(X_test)\n", - "\n", - " # Evaluate the model (you can customize this based on your problem)\n", - " accuracy = accuracy_score(y_test, y_pred)\n", - " print(f'Accuracy: {accuracy}')\n", - "\n", - " # Optionally, you can perform cross-validation\n", - " cross_val_scores = cross_val_score(model, X, y, cv=5) # 5-fold cross-validation\n", - " print(f'Cross-Validation Scores: {cross_val_scores}')\n", - " print(f'Mean Cross-Validation Score: {cross_val_scores.mean()}')" - ] - }, - { - "cell_type": "code", - "execution_count": 30, - "id": "14ea9841", - "metadata": {}, - "outputs": [], - "source": [ - "from sklearn.tree import plot_tree" - ] - }, - { - "cell_type": "code", - "execution_count": 31, - "id": "cffe1ad1", - "metadata": {}, - "outputs": [], - "source": [ - "def run_multi_column_prediction_model_with_tree_plot(df):\n", - " # Initialize your model (replace RandomForestClassifier with the model of your choice)\n", - " model = RandomForestClassifier()\n", - "\n", - " for column in df.columns[:-1]: # Exclude the last column, assumed to be the label\n", - " print(f'\\nTraining and evaluating model for predicting: {column}')\n", - "\n", - " # Features and label\n", - " X = df.drop(column, axis=1) # Features\n", - " y = df[column] # Label\n", - "\n", - " # Split the data into training and testing sets\n", - " X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=42)\n", - "\n", - " # Train the model\n", - " model.fit(X_train, y_train)\n", - "\n", - " # Make predictions on the test set\n", - " y_pred = model.predict(X_test)\n", - "\n", - " # Plot one of the trees in the forest (you can customize this)\n", - " plt.figure(figsize=(12, 8))\n", - " plot_tree(model.estimators_[0], feature_names=X.columns, class_names=[str(c) for c in model.classes_], filled=True)\n", - " plt.title(f'Decision Tree for predicting: {column}')\n", - " plt.show()\n", - "\n", - " # Rest of the evaluation can remain the same\n", - " # Evaluate the model (you can customize this based on your problem)\n", - " accuracy = accuracy_score(y_test, y_pred)\n", - " print(f'Accuracy: {accuracy}')" - ] - }, - { - "cell_type": "code", - "execution_count": 32, - "id": "c5243c59", - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "\n", - "Training and evaluating model for predicting: salary\n" - ] - }, - { - "ename": "ValueError", - "evalue": "Unknown label type: 'continuous'", - "output_type": "error", - "traceback": [ - "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m", - "\u001b[1;31mValueError\u001b[0m Traceback (most recent call last)", - "Input \u001b[1;32mIn [32]\u001b[0m, in \u001b[0;36m\u001b[1;34m()\u001b[0m\n\u001b[1;32m----> 1\u001b[0m \u001b[43mrun_multi_column_prediction_model_with_tree_plot\u001b[49m\u001b[43m(\u001b[49m\u001b[43mdf\u001b[49m\u001b[43m)\u001b[49m\n", - "Input \u001b[1;32mIn [31]\u001b[0m, in \u001b[0;36mrun_multi_column_prediction_model_with_tree_plot\u001b[1;34m(df)\u001b[0m\n\u001b[0;32m 13\u001b[0m X_train, X_test, y_train, y_test \u001b[38;5;241m=\u001b[39m train_test_split(X, y, test_size\u001b[38;5;241m=\u001b[39m\u001b[38;5;241m0.2\u001b[39m, random_state\u001b[38;5;241m=\u001b[39m\u001b[38;5;241m42\u001b[39m)\n\u001b[0;32m 15\u001b[0m \u001b[38;5;66;03m# Train the model\u001b[39;00m\n\u001b[1;32m---> 16\u001b[0m \u001b[43mmodel\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mfit\u001b[49m\u001b[43m(\u001b[49m\u001b[43mX_train\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43my_train\u001b[49m\u001b[43m)\u001b[49m\n\u001b[0;32m 18\u001b[0m \u001b[38;5;66;03m# Make predictions on the test set\u001b[39;00m\n\u001b[0;32m 19\u001b[0m y_pred \u001b[38;5;241m=\u001b[39m model\u001b[38;5;241m.\u001b[39mpredict(X_test)\n", - "File \u001b[1;32m~\\anaconda3\\lib\\site-packages\\sklearn\\ensemble\\_forest.py:367\u001b[0m, in \u001b[0;36mBaseForest.fit\u001b[1;34m(self, X, y, sample_weight)\u001b[0m\n\u001b[0;32m 360\u001b[0m \u001b[38;5;28;01mraise\u001b[39;00m \u001b[38;5;167;01mValueError\u001b[39;00m(\n\u001b[0;32m 361\u001b[0m \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mSum of y is not strictly positive which \u001b[39m\u001b[38;5;124m\"\u001b[39m\n\u001b[0;32m 362\u001b[0m \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mis necessary for Poisson regression.\u001b[39m\u001b[38;5;124m\"\u001b[39m\n\u001b[0;32m 363\u001b[0m )\n\u001b[0;32m 365\u001b[0m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mn_outputs_ \u001b[38;5;241m=\u001b[39m y\u001b[38;5;241m.\u001b[39mshape[\u001b[38;5;241m1\u001b[39m]\n\u001b[1;32m--> 367\u001b[0m y, expanded_class_weight \u001b[38;5;241m=\u001b[39m \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43m_validate_y_class_weight\u001b[49m\u001b[43m(\u001b[49m\u001b[43my\u001b[49m\u001b[43m)\u001b[49m\n\u001b[0;32m 369\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;28mgetattr\u001b[39m(y, \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mdtype\u001b[39m\u001b[38;5;124m\"\u001b[39m, \u001b[38;5;28;01mNone\u001b[39;00m) \u001b[38;5;241m!=\u001b[39m DOUBLE \u001b[38;5;129;01mor\u001b[39;00m \u001b[38;5;129;01mnot\u001b[39;00m y\u001b[38;5;241m.\u001b[39mflags\u001b[38;5;241m.\u001b[39mcontiguous:\n\u001b[0;32m 370\u001b[0m y \u001b[38;5;241m=\u001b[39m np\u001b[38;5;241m.\u001b[39mascontiguousarray(y, dtype\u001b[38;5;241m=\u001b[39mDOUBLE)\n", - "File \u001b[1;32m~\\anaconda3\\lib\\site-packages\\sklearn\\ensemble\\_forest.py:734\u001b[0m, in \u001b[0;36mForestClassifier._validate_y_class_weight\u001b[1;34m(self, y)\u001b[0m\n\u001b[0;32m 733\u001b[0m \u001b[38;5;28;01mdef\u001b[39;00m \u001b[38;5;21m_validate_y_class_weight\u001b[39m(\u001b[38;5;28mself\u001b[39m, y):\n\u001b[1;32m--> 734\u001b[0m \u001b[43mcheck_classification_targets\u001b[49m\u001b[43m(\u001b[49m\u001b[43my\u001b[49m\u001b[43m)\u001b[49m\n\u001b[0;32m 736\u001b[0m y \u001b[38;5;241m=\u001b[39m np\u001b[38;5;241m.\u001b[39mcopy(y)\n\u001b[0;32m 737\u001b[0m expanded_class_weight \u001b[38;5;241m=\u001b[39m \u001b[38;5;28;01mNone\u001b[39;00m\n", - "File \u001b[1;32m~\\anaconda3\\lib\\site-packages\\sklearn\\utils\\multiclass.py:197\u001b[0m, in \u001b[0;36mcheck_classification_targets\u001b[1;34m(y)\u001b[0m\n\u001b[0;32m 189\u001b[0m y_type \u001b[38;5;241m=\u001b[39m type_of_target(y)\n\u001b[0;32m 190\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m y_type \u001b[38;5;129;01mnot\u001b[39;00m \u001b[38;5;129;01min\u001b[39;00m [\n\u001b[0;32m 191\u001b[0m \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mbinary\u001b[39m\u001b[38;5;124m\"\u001b[39m,\n\u001b[0;32m 192\u001b[0m \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mmulticlass\u001b[39m\u001b[38;5;124m\"\u001b[39m,\n\u001b[1;32m (...)\u001b[0m\n\u001b[0;32m 195\u001b[0m \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mmultilabel-sequences\u001b[39m\u001b[38;5;124m\"\u001b[39m,\n\u001b[0;32m 196\u001b[0m ]:\n\u001b[1;32m--> 197\u001b[0m \u001b[38;5;28;01mraise\u001b[39;00m \u001b[38;5;167;01mValueError\u001b[39;00m(\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mUnknown label type: \u001b[39m\u001b[38;5;132;01m%r\u001b[39;00m\u001b[38;5;124m\"\u001b[39m \u001b[38;5;241m%\u001b[39m y_type)\n", - "\u001b[1;31mValueError\u001b[0m: Unknown label type: 'continuous'" - ] - } - ], - "source": [ - "run_multi_column_prediction_model_with_tree_plot(df)" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "cd637808", - "metadata": {}, - "outputs": [], - "source": [] - } - ], - "metadata": { - "kernelspec": { - "display_name": "Python 3 (ipykernel)", - "language": "python", - "name": "python3" - }, - "language_info": { - "codemirror_mode": { - "name": "ipython", - "version": 3 - }, - "file_extension": ".py", - "mimetype": "text/x-python", - "name": "python", - "nbconvert_exporter": "python", - "pygments_lexer": "ipython3", - "version": "3.9.12" - } - }, - "nbformat": 4, - "nbformat_minor": 5 -}