diff --git a/Machine_Learning/Absent_Time_prediction_using_HyperParameter_Tuning/HyperParameter Tuning.md b/Machine_Learning/Absent_Time_prediction_using_HyperParameter_Tuning/HyperParameter Tuning.md new file mode 100644 index 0000000000..1cec5f67b5 --- /dev/null +++ b/Machine_Learning/Absent_Time_prediction_using_HyperParameter_Tuning/HyperParameter Tuning.md @@ -0,0 +1,294 @@ +# Hyperparameter Tuning + +Hyperparameter tuning is a crucial step in machine learning model development. It involves finding the optimal combination of hyperparameters that results in the best performance of the model. In this resource, we will explore two popular methods for hyperparameter tuning: **GridSearchCV** and **RandomizedSearchCV**. + +## GridSearchCV + +GridSearchCV is a method for hyperparameter tuning that involves exhaustively searching through a grid of possible hyperparameter combinations. It is a brute-force approach that tries all possible combinations of hyperparameters and evaluates the model's performance on each combination. + +### How GridSearchCV Works + +GridSearchCV works by defining a grid of possible hyperparameter combinations, splitting the data into training and validation sets, performing a grid search over the hyperparameter grid, and selecting the hyperparameters that result in the best performance on the validation data. + +### Advantages of GridSearchCV + +- Exhaustive search ensures that the optimal hyperparameters are found. +- Easy to implement and interpret. + +### Disadvantages of GridSearchCV + +- Computationally expensive, especially for large hyperparameter spaces. +- May not be feasible for high-dimensional hyperparameter spaces. + +## RandomizedSearchCV + +RandomizedSearchCV is a method for hyperparameter tuning that involves randomly sampling the hyperparameter space. It is a more efficient approach than GridSearchCV, especially when the hyperparameter space is large. + +### How RandomizedSearchCV Works + +RandomizedSearchCV works by defining a distribution for each hyperparameter, splitting the data into training and validation sets, performing a randomized search over the hyperparameter space, and selecting the hyperparameters that result in the best performance on the validation data. + +### Advantages of RandomizedSearchCV + +- More efficient than GridSearchCV, especially for large hyperparameter spaces. +- Can handle high-dimensional hyperparameter spaces. + +### Disadvantages of RandomizedSearchCV + +- May not find the optimal hyperparameters due to the random nature of the search. +- Requires careful tuning of the hyperparameter distributions. + +## Comparison of GridSearchCV and RandomizedSearchCV + +| Feature | GridSearchCV | RandomizedSearchCV | +|------------------------|---------------------|----------------------| +| Search strategy | Exhaustive search | Random search | +| Computational complexity | High | Low | +| Hyperparameter space | Discrete | Continuous or discrete| +| Number of iterations | Fixed | Variable | + +### When to use each method + +**Use GridSearchCV when:** +- The hyperparameter space is small. +- You want to exhaustively search the hyperparameter space. + +**Use RandomizedSearchCV when:** +- The hyperparameter space is large. +- You want to perform a more efficient search. + +## Best Practices for Hyperparameter Tuning + +- Use a combination of GridSearchCV and RandomizedSearchCV to leverage the strengths of both methods. +- Use cross-validation to evaluate the model's performance on unseen data. +- Use a robust evaluation metric to avoid overfitting. +- Perform hyperparameter tuning on a subset of the data to reduce computational complexity. +- Use techniques such as early stopping and learning rate scheduling to improve the efficiency of the search. + +## Hyperparameter Tuning Strategies + +### Grid Search Strategies + +- **Full Grid Search:** Exhaustively search the entire hyperparameter space. +- **Random Grid Search:** Randomly sample the hyperparameter space and perform a grid search on the sampled points. +- **Grid Search with Bayesian Optimization:** Use Bayesian optimization to guide the grid search and focus on the most promising regions of the hyperparameter space. + +### Random Search Strategies + +- **Uniform Random Search:** Randomly sample the hyperparameter space using a uniform distribution. +- **Non-Uniform Random Search:** Randomly sample the hyperparameter space using a non-uniform distribution (e.g., normal, lognormal, etc.). +- **Random Search with Bayesian Optimization:** Use Bayesian optimization to guide the random search and focus on the most promising regions of the hyperparameter space. + +### Hybrid Strategies + +- **Grid Search with Random Search:** Perform a grid search on a subset of the hyperparameter space and then use random search to explore the remaining space. +- **Random Search with Grid Search:** Perform a random search on the entire hyperparameter space and then use grid search to refine the search in the most promising regions. + +## Hyperparameter Tuning for Deep Learning Models + +### Challenges of Hyperparameter Tuning for Deep Learning Models + +- **High-dimensional hyperparameter space:** Deep learning models have a large number of hyperparameters, making it challenging to perform hyperparameter tuning. +- **Computational complexity:** Training deep learning models is computationally expensive, making it challenging to perform hyperparameter tuning using traditional methods. + +### Strategies for Hyperparameter Tuning for Deep Learning Models + +- **Transfer learning:** Use pre-trained models as a starting point for hyperparameter tuning. +- **Hyperparameter tuning using proxy tasks:** Use proxy tasks (e.g., image classification on a smaller dataset) to perform hyperparameter tuning and then transfer the learned hyperparameters to the target task. +- **Hyperparameter tuning using Bayesian optimization:** Use Bayesian optimization to guide the hyperparameter tuning process and focus on the most promising regions of the hyperparameter space. + +## Conclusion + +Hyperparameter tuning is a crucial step in machine learning model development. GridSearchCV and RandomizedSearchCV are two popular methods for hyperparameter tuning, each with its own strengths and weaknesses. By understanding the theoretical aspects of these methods and using best practices for hyperparameter tuning, machine learning practitioners can develop more accurate and efficient models. + +## Use Case: GridSearchCV + +```python +from sklearn.model_selection import GridSearchCV +from sklearn.tree import DecisionTreeRegressor +from sklearn.ensemble import ( + RandomForestRegressor, GradientBoostingRegressor, + ExtraTreesRegressor, VotingRegressor +) +from sklearn.svm import SVR +from sklearn.ensemble import BaggingRegressor, AdaBoostRegressor +from sklearn.neural_network import MLPRegressor +from sklearn.metrics import make_scorer, r2_score, mean_squared_error +from sklearn.model_selection import train_test_split +from sklearn.linear_model import Ridge + +X_train, X_test, Y_train, Y_test = train_test_split( + df.drop(columns='Absenteeism time in hours'), + df['Absenteeism time in hours'], + test_size=0.2, + random_state=4 +) + +r2_scorer = make_scorer(r2_score, greater_is_better=True) +mse_scorer = make_scorer(mean_squared_error, greater_is_better=False) + +models = { + 'ridge': Ridge(), + 'decision_tree': DecisionTreeRegressor(), + 'random_forest': RandomForestRegressor(), + 'extra_trees': ExtraTreesRegressor(), + 'gradient_boosting': GradientBoostingRegressor(), + 'mlp': MLPRegressor(), + 'bagging': BaggingRegressor(), + 'adaboost': AdaBoostRegressor(), + 'svr': SVR(), + 'vote': VotingRegressor(estimators=[ + ('ridge', Ridge()), + ('decision_tree', DecisionTreeRegressor()), + ('random_forest', RandomForestRegressor()), + ('extra_trees', ExtraTreesRegressor()), + ('gradient_boosting', GradientBoostingRegressor()), + ('mlp', MLPRegressor()), + ('bagging', BaggingRegressor()), + ('adaboost', AdaBoostRegressor()), + ('svr', SVR()) + ]) +} + +from sklearn.model_selection import cross_val_score + +def cross_val_scores(model_name, model): + print(f"Running cross-validation for {model_name}...") + scores_r2 = cross_val_score(model, X_train, Y_train, cv=5, scoring=r2_scorer) + scores_mse = cross_val_score(model, X_train, Y_train, cv=5, scoring=mse_scorer) + print(f"Cross-validation R2 scores for {model_name}: {scores_r2}") + print(f"Cross-validation MSE scores for {model_name}: {scores_mse}") + print(f"Average cross-validation R2 score for {model_name}: {scores_r2.mean()}") + print(f"Average cross-validation MSE score for {model_name}: {scores_mse.mean()}") + +for model_name, model in models.items(): + cross_val_scores(model_name, model) + Y_pred = model.predict(X_test) + r2 = r2_score(Y_test, Y_pred) + print(f"R2 Score for {model_name} on test set: {r2}") + + best_model_name = max(best_models, key=lambda x: r2_score(Y_test, best_models[x].predict(X_test))) + print(f"Best model: {best_model_name}") + + Y_pred = best_models[best_model_name].predict(X_test) + r2 = r2_score(Y_test, Y_pred) + print(f"R2 Score for best model on test set: {r2}") +``` + + +## Use Case: RandomizedSearchCV + +```python + +from sklearn.model_selection import RandomizedSearchCV +from sklearn.tree import DecisionTreeRegressor +from sklearn.ensemble import ( + RandomForestRegressor, GradientBoostingRegressor, + ExtraTreesRegressor, BaggingRegressor, AdaBoostRegressor +) +from sklearn.svm import SVR +from sklearn.neural_network import MLPRegressor +from sklearn.metrics import make_scorer, r2_score +from sklearn.model_selection import train_test_split +from sklearn.linear_model import Ridge + +X_train, X_test, Y_train, Y_test = train_test_split( + df.drop(columns='Absenteeism time in hours'), + df['Absenteeism time in hours'], + test_size=0.2, + random_state=4 +) + +scorer = make_scorer(r2_score) + +param_distributions = { + 'ridge': { + 'alpha': [0.05, 0.01, 0.05, 0.1, 0.5, 1.0, 5.0, 10.0], + 'solver': ['svd', 'cholesky'] + }, + 'decision_tree': { + 'max_depth': [3, 5, 7, 10, 15, 20, 25], + 'min_samples_split': [2, 5, 7, 10], + 'min_samples_leaf': [1, 2 +, 4, 5], + 'criterion': ['absolute_error', 'poisson', 'squared_error', 'friedman_mse'] + }, + 'random_forest': { + 'n_estimators': [100, 250, 500, 1000, 2000, 5000, 10000], + 'max_depth': [5, 7, 10, 15, 20, 25], + 'min_samples_split': [2, 5, 7, 10], + 'min_samples_leaf': [1, 2, 4, 5], + 'criterion': ['absolute_error', 'poisson', 'squared_error', 'friedman_mse'] + }, + 'extra_trees': { + 'n_estimators': [100, 250, 500, 1000, 2000, 5000, 10000], + 'max_depth': [5, 7, 10, 15, 20, 25], + 'min_samples_split': [2, 5, 7, 10], + 'min_samples_leaf': [1, 2, 4, 5], + 'criterion': ['absolute_error', 'poisson', 'squared_error', 'friedman_mse'] + }, + 'gradient_boosting': { + 'n_estimators': [100, 250, 500, 1000, 2000, 5000, 10000], + 'learning_rate': [0.001, 0.005, 0.01, 0.05, 0.1], + 'max_depth': [3, 5, 7, 10, 15, 20, 25], + 'subsample': [0.8, 0.9, 1.0] + }, + 'svr': { + 'kernel': ['poly', 'sigmoid', 'rbf', 'linear'], + 'C': [1e0, 1e2, 1e4, 1e6, 1e8, 1e10], + 'epsilon': [0.1, 0.5, 1.0, 5.0, 10.0] + }, + 'mlp': { + 'hidden_layer_sizes': [(50, 50), (100, 100), (200, 200), (500, 500), (1000, 1000)], + 'activation': ['relu', 'tanh', 'sigmoid'], + 'solver': ['adam', 'sgd'], + 'alpha': [0.001, 0.01, 0.05, 0.1, 0.5, 1.0] + }, + 'bagging': { + 'n_estimators': [100, 250, 500, 1000, 2000, 5000, 10000], + 'max_samples': [0.2, 0.5, 0.8, 1.0], + 'max_features': [0.2, 0.5, 0.8, 1.0] + }, + 'adaboost': { + 'n_estimators': [100, 250, 500, 1000, 2000, 5000, 10000], + 'learning_rate': [0.001, 0.01, 0.05, 0.1] + } +} + +models = { + 'ridge': Ridge(), + 'decision_tree': DecisionTreeRegressor(), + 'random_forest': RandomForestRegressor(), + 'extra_trees': ExtraTreesRegressor(), + 'gradient_boosting': GradientBoostingRegressor(), + 'mlp': MLPRegressor(), + 'bagging': BaggingRegressor(), + 'adaboost': AdaBoostRegressor(), + 'svr': SVR(), +} + +best_models = {} + +def random_search_model(model_name, model, param_distribution): + print(f"Running RandomizedSearchCV for {model_name}...") + random_search = RandomizedSearchCV(estimator=model, param_distributions=param_distribution, cv=5, n_iter=10, random_state=42, scoring=scorer) + random_search.fit(X_train, Y_train) + print(f"Best parameters for {model_name}: {random_search.best_params_}") + print(f"Best R2 Score for {model_name}: {random_search.best_score_}") + return random_search.best_estimator_ + +for model_name in models: + best_models[model_name] = random_search_model(model_name, models[model_name], param_distributions[model_name]) + +for model_name, model in best_models.items(): + Y_pred = model.predict(X_test) + r2 = r2_score(Y_test, Y_pred) + print(f"R2 Score for {model_name} on test set: {r2}") + +best_model_name = max(best_models, key=lambda x: r2_score(Y_test, best_models[x].predict(X_test))) +print(f"Best model: {best_model_name}") + +Y_pred = best_models[best_model_name].predict(X_test) +r2 = r2_score(Y_test, Y_pred) +print(f"R2 Score for best model on test set: {r2}") +``` \ No newline at end of file diff --git a/Machine_Learning/Absent_Time_prediction_using_HyperParameter_Tuning/Model.md b/Machine_Learning/Absent_Time_prediction_using_HyperParameter_Tuning/Model.md new file mode 100644 index 0000000000..a8ddf05a5a --- /dev/null +++ b/Machine_Learning/Absent_Time_prediction_using_HyperParameter_Tuning/Model.md @@ -0,0 +1,40 @@ +# Justification for Irrelevant Model prediction + +## Overview +Despite utilizing advanced techniques such as RandomizedSearchCV and GridSearchCV on the complete dataset, along with Principal Component Analysis (PCA) for feature reduction and feature selection via L1 regularization, the resulting evaluation scores do not meet the required threshold for model performance. This document aims to provide a detailed justification for these findings. + +## Key Techniques Utilized + +1. **RandomizedSearchCV and GridSearchCV**: + - Both techniques were employed to optimize hyperparameters systematically. + - RandomizedSearchCV allows for a broader search of the hyperparameter space, while GridSearchCV exhaustively tests predefined parameter combinations. + - Despite extensive tuning, neither method yielded satisfactory results. + +2. **PCA for Feature Reduction**: + - PCA was applied to reduce dimensionality and eliminate multicollinearity among features. + - While PCA can enhance model performance by simplifying the feature space, it can also lead to loss of interpretability and potentially important variance. + +3. **Feature Selection via L1 Regularization**: + - L1 regularization was implemented to select relevant features and mitigate overfitting. + - This method can enhance model generalization but may exclude features that contain valuable information necessary for predicting target variables. + +## Observations + +### 1. Model Complexity and undefitting +- The models trained may be not relative to the simplicity of the underlying data structure. +- Linear models, while interpretable, may not capture complex relationships inherent in the data. + +### 2. Data Quality and Feature Engineering +- The quality and richness of the dataset play crucial roles in model performance. Issues such as: + - Missing values + - Outliers + - Noise in the data +- If the dataset does not adequately represent the underlying patterns, even well-tuned models may fail to achieve realistic evaluation metrics. + +### 4. Potential Overfitting with Feature Selection +- L1 regularization can lead to overfitting, especially when the dataset is small relative to the number of features. +- The features selected might not generalize well to unseen data, contributing to poor performance. + +## Conclusion + +Despite leveraging sophisticated techniques such as RandomizedSearchCV, GridSearchCV, PCA, and L1 regularization, the models developed have not met the desired R² score benchmarks. This may be attributed to a combination of model complexity issues, data quality, **Co-relation between Features and target** and potential *overfitting/underfitting*. \ No newline at end of file diff --git a/Machine_Learning/Absent_Time_prediction_using_HyperParameter_Tuning/absenteeism_at_work_data/Absenteeism_at_work.arff b/Machine_Learning/Absent_Time_prediction_using_HyperParameter_Tuning/absenteeism_at_work_data/Absenteeism_at_work.arff new file mode 100644 index 0000000000..05a82ef6bf --- /dev/null +++ b/Machine_Learning/Absent_Time_prediction_using_HyperParameter_Tuning/absenteeism_at_work_data/Absenteeism_at_work.arff @@ -0,0 +1,765 @@ +@relation Absenteeism_at_work_A + +@attribute ID {31.0, 27.0, 19.0, 30.0, 7.0, 20.0, 24.0, 32.0, 3.0, 33.0, 26.0, 29.0, 18.0, 25.0, 17.0, 14.0, 16.0, 23.0, 2.0, 21.0, 36.0, 15.0, 22.0, 5.0, 12.0, 9.0, 6.0, 34.0, 10.0, 28.0, 13.0, 11.0, 1.0, 4.0, 8.0, 35.0} +@attribute Reason_for_absence {17.0, 3.0, 15.0, 4.0, 21.0, 2.0, 9.0, 24.0, 18.0, 1.0, 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0000000000..1b925786d9 Binary files /dev/null and b/Machine_Learning/Absent_Time_prediction_using_HyperParameter_Tuning/images/pairplot.png differ diff --git a/Machine_Learning/Absent_Time_prediction_using_HyperParameter_Tuning/images/plot.png b/Machine_Learning/Absent_Time_prediction_using_HyperParameter_Tuning/images/plot.png new file mode 100644 index 0000000000..5585d6c9cb Binary files /dev/null and b/Machine_Learning/Absent_Time_prediction_using_HyperParameter_Tuning/images/plot.png differ diff --git a/Machine_Learning/Absent_Time_prediction_using_HyperParameter_Tuning/model/Absent_time.ipynb b/Machine_Learning/Absent_Time_prediction_using_HyperParameter_Tuning/model/Absent_time.ipynb new file mode 100644 index 0000000000..042d3068fc --- /dev/null +++ b/Machine_Learning/Absent_Time_prediction_using_HyperParameter_Tuning/model/Absent_time.ipynb @@ -0,0 +1,1756 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "id": "f708f343-bba6-4b4a-8a9c-104122a43609", + "metadata": {}, + "source": [ + "### Problem Statement : **Predict for how long an employee will be absent based on features given in the dataset.**" + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "id": "bdcc864a-f9cd-46d0-b352-02c04dbc3c75", + "metadata": {}, + "outputs": [], + "source": [ + "import numpy as np\n", + "import pandas as pd" + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "id": "5b2ea2fb-0817-4080-9462-1287e7f65259", + "metadata": {}, + "outputs": [], + "source": [ + "dataset = pd.read_csv(\"absenteeism_at_work/Absenteeism_at_work.csv\")\n", + "dataset.head()" + ] + }, + { + "cell_type": "code", + "execution_count": 4, + "id": "8f0169b6-7694-4c84-b978-fe2a62578e9b", + "metadata": {}, + "outputs": [], + "source": [ + "dataset.shape" + ] + }, + { + "cell_type": "code", + "execution_count": 5, + "id": "d0d8e49e-113e-4b57-8133-87ba29fef07c", + "metadata": {}, + "outputs": [], + "source": [ + "for index, row in dataset.iterrows():\n", + " print(row)" + ] + }, + { + "cell_type": "code", + "execution_count": 6, + "id": "e4ad78e1-ddaf-430a-a105-803d0ca15ee5", + "metadata": {}, + "outputs": [], + "source": [ + "for index, row in dataset.iterrows():\n", + " values = row['ID;Reason for absence;Month of absence;Day of the week;Seasons;Transportation expense;Distance from Residence to Work;Service time;Age;Work load Average/day ;Hit target;Disciplinary failure;Education;Son;Social drinker;Social smoker;Pet;Weight;Height;Body mass index;Absenteeism time in hours'].split(';')\n", + " dataset.loc[index, 'ID'] = values[0]\n", + " dataset.loc[index, 'Reason for absence'] = values[1]\n", + " dataset.loc[index, 'Month of absence'] = values[2]\n", + " dataset.loc[index, 'Day of the week'] = values[3]\n", + " dataset.loc[index, 'Seasons'] = values[4]\n", + " dataset.loc[index, 'Transportation expense'] = values[5]\n", + " dataset.loc[index, 'Distance from Residence to Work'] = values[6]\n", + " dataset.loc[index, 'Service time'] = values[7]\n", + " dataset.loc[index, 'Age'] = values[8]\n", + " dataset.loc[index, 'Work load Average/day'] = values[9]\n", + " dataset.loc[index, 'Hit target'] = values[10]\n", + " dataset.loc[index, 'Disciplinary failure'] = values[11]\n", + " dataset.loc[index, 'Education'] = values[12]\n", + " dataset.loc[index, 'Son'] = values[13]\n", + " dataset.loc[index, 'Social drinker'] = values[14]\n", + " dataset.loc[index, 'Social smoker'] = values[15]\n", + " dataset.loc[index, 'Pet'] = values[16]\n", + " dataset.loc[index, 'Weight'] = values[17]\n", + " dataset.loc[index, 'Height'] = values[18]\n", + " dataset.loc[index, 'Body mass index'] = values[19]\n", + " dataset.loc[index, 'Absenteeism time in hours'] = values[20]" + ] + }, + { + "cell_type": "code", + "execution_count": 7, + "id": "cc1872a2-03e7-4925-b472-69288e4e3b61", + "metadata": {}, + "outputs": [], + "source": [ + "dataset.head()" + ] + }, + { + "cell_type": "code", + "execution_count": 8, + "id": "6d68aaf1-f4d4-4d36-9269-d1e326a69989", + "metadata": {}, + "outputs": [], + "source": [ + "df = dataset.drop(columns = 'ID;Reason for absence;Month of absence;Day of the week;Seasons;Transportation expense;Distance from Residence to Work;Service time;Age;Work load Average/day ;Hit target;Disciplinary failure;Education;Son;Social drinker;Social smoker;Pet;Weight;Height;Body mass index;Absenteeism time in hours')" + ] + }, + { + "cell_type": "code", + "execution_count": 9, + "id": "8490b778-eb38-48a4-ae08-37c71ddc936c", + "metadata": {}, + "outputs": [], + "source": [ + "df.head()" + ] + }, + { + "cell_type": "code", + "execution_count": 10, + "id": "01d652b1-c725-4372-b273-4f7acfba37e9", + "metadata": {}, + "outputs": [], + "source": [ + "df.shape" + ] + }, + { + "cell_type": "code", + "execution_count": 11, + "id": "0e255d68-866a-4aa1-bd66-aa16b7266a5b", + "metadata": {}, + "outputs": [], + "source": [ + "df.info()" + ] + }, + { + "cell_type": "raw", + "id": "e4fb8b2b-8a20-47ce-8a11-bb9a9b753529", + "metadata": {}, + "source": [ + "Attribute Information:\n", + "\n", + "1. Individual identification (ID)\n", + "2. Reason for absence (ICD).\n", + "Absences attested by the International Code of Diseases (ICD) stratified into 21 categories (I to XXI) as follows:\n", + "\n", + "I Certain infectious and parasitic diseases \n", + "II Neoplasms \n", + "III Diseases of the blood and blood-forming organs and certain disorders involving the immune mechanism \n", + "IV Endocrine, nutritional and metabolic diseases \n", + "V Mental and behavioural disorders \n", + "VI Diseases of the nervous system \n", + "VII Diseases of the eye and adnexa \n", + "VIII Diseases of the ear and mastoid process \n", + "IX Diseases of the circulatory system \n", + "X Diseases of the respiratory system \n", + "XI Diseases of the digestive system \n", + "XII Diseases of the skin and subcutaneous tissue \n", + "XIII Diseases of the musculoskeletal system and connective tissue \n", + "XIV Diseases of the genitourinary system \n", + "XV Pregnancy, childbirth and the puerperium \n", + "XVI Certain conditions originating in the perinatal period \n", + "XVII Congenital malformations, deformations and chromosomal abnormalities \n", + "XVIII Symptoms, signs and abnormal clinical and laboratory findings, not elsewhere classified \n", + "XIX Injury, poisoning and certain other consequences of external causes \n", + "XX External causes of morbidity and mortality \n", + "XXI Factors influencing health status and contact with health services.\n", + "\n", + "And 7 categories without (CID) patient follow-up (22), medical consultation (23), blood donation (24), laboratory examination (25), unjustified absence (26), physiotherapy (27), dental consultation (28).\n", + "3. Month of absence\n", + "4. Day of the week (Monday (2), Tuesday (3), Wednesday (4), Thursday (5), Friday (6))\n", + "5. Seasons\n", + "6. Transportation expense\n", + "7. Distance from Residence to Work (kilometers)\n", + "8. Service time\n", + "9. Age\n", + "10. Work load Average/day \n", + "11. Hit target\n", + "12. Disciplinary failure (yes=1; no=0)\n", + "13. Education (high school (1), graduate (2), postgraduate (3), master and doctor (4))\n", + "14. Son (number of children)\n", + "15. Social drinker (yes=1; no=0)\n", + "16. Social smoker (yes=1; no=0)\n", + "17. Pet (number of pet)\n", + "18. Weight\n", + "19. Height\n", + "20. Body mass index\n", + "21. Absenteeism time in hours (target)\n", + "\n", + ".arff header for Weka: \n", + "\n", + "@relation Absenteeism_at_work\n", + "\n", + "@attribute ID {31.0, 27.0, 19.0, 30.0, 7.0, 20.0, 24.0, 32.0, 3.0, 33.0, 26.0, 29.0, 18.0, 25.0, 17.0, 14.0, 16.0, 23.0, 2.0, 21.0, 36.0, 15.0, 22.0, 5.0, 12.0, 9.0, 6.0, 34.0, 10.0, 28.0, 13.0, 11.0, 1.0, 4.0, 8.0, 35.0}\n", + "@attribute Reason_for_absence {17.0, 3.0, 15.0, 4.0, 21.0, 2.0, 9.0, 24.0, 18.0, 1.0, 12.0, 5.0, 16.0, 7.0, 27.0, 25.0, 8.0, 10.0, 26.0, 19.0, 28.0, 6.0, 23.0, 22.0, 13.0, 14.0, 11.0, 0.0}\n", + "@attribute Month_of_absence REAL\n", + "@attribute Day_of_the_week {5.0, 2.0, 3.0, 4.0, 6.0}\n", + "@attribute Seasons {4.0, 1.0, 2.0, 3.0}\n", + "@attribute Transportation_expense REAL\n", + "@attribute Distance_from_Residence_to_Work REAL\n", + "@attribute Service_time INTEGER\n", + "@attribute Age INTEGER\n", + "@attribute Work_load_Average/day_ REAL\n", + "@attribute Hit_target REAL\n", + "@attribute Disciplinary_failure {1.0, 0.0}\n", + "@attribute Education REAL\n", + "@attribute Son REAL\n", + "@attribute Drinker {1.0, 0.0}\n", + "@attribute Smoker {1.0, 0.0}\n", + "@attribute Pet REAL\n", + "@attribute Weight REAL\n", + "@attribute Height REAL\n", + "@attribute Body_mass_index REAL\n", + "@attribute Absenteeism_time_in_hours REAL" + ] + }, + { + "cell_type": "code", + "execution_count": 12, + "id": "fe919764-3710-4dc9-83bd-2d6eebbfffb6", + "metadata": {}, + "outputs": [], + "source": [ + "df['ID'] = dataset['ID'].astype(float)\n", + "df['Reason for absence'] = dataset['Reason for absence'].astype(float)\n", + "df['Day of the week'] = dataset['Day of the week'].astype(float)\n", + "df['Month of absence'] = dataset['Month of absence'].astype(float)\n", + "df['Seasons'] = dataset['Seasons'].astype(float)\n", + "df['Transportation expense'] = dataset['Transportation expense'].astype(float)\n", + "df['Distance from Residence to Work'] = dataset['Distance from Residence to Work'].astype(float)\n", + "df['Service time'] = dataset['Service time'].astype(float)\n", + "df['Age'] = dataset['Age'].astype(int)\n", + "df['Work load Average/day'] = df['Work load Average/day'].astype(float)\n", + "df['Hit target'] = dataset['Hit target'].astype(float)\n", + "df['Disciplinary failure'] = dataset['Disciplinary failure'].astype(float).astype(float)\n", + "df['Education'] = dataset['Education'].astype(float)\n", + "df['Son'] = dataset['Son'].astype(float)\n", + "#dataset['Social drinker']\n", + "#dataset['Social smoker']\n", + "df['Pet'] = dataset['Pet'].astype(float)\n", + "df['Weight'] = dataset['Weight'].astype(float)\n", + "df['Height'] = dataset['Height'].astype(float)\n", + "df['Body mass index'] = dataset['Body mass index'].astype(float)\n", + "df['Absenteeism time in hours'] = dataset['Absenteeism time in hours'].astype(float)" + ] + }, + { + "cell_type": "code", + "execution_count": 13, + "id": "a9d21923-2d1f-40ef-8fb9-0ce365a342d4", + "metadata": {}, + "outputs": [], + "source": [ + "df.head()" + ] + }, + { + "cell_type": "code", + "execution_count": 14, + "id": "094c2be5-f75e-491d-9304-d0bd921ce7e7", + "metadata": {}, + "outputs": [], + "source": [ + "df['Work load Average/day'] = df['Work load Average/day']*1000" + ] + }, + { + "cell_type": "code", + "execution_count": 15, + "id": "cb59404d-b6be-4cb9-9271-f6f936855051", + "metadata": {}, + "outputs": [], + "source": [ + "df.head(10)" + ] + }, + { + "cell_type": "code", + "execution_count": 16, + "id": "9ca20fa0-5b0a-4d94-a8a5-ec1ceda22fbe", + "metadata": {}, + "outputs": [], + "source": [ + "df['Work load Average/day'] = df['Work load Average/day'].astype(int)" + ] + }, + { + "cell_type": "code", + "execution_count": 17, + "id": "ee934833-627b-42e9-9464-23fac35fd1dd", + "metadata": {}, + "outputs": [], + "source": [ + "df.head()" + ] + }, + { + "cell_type": "code", + "execution_count": 18, + "id": "7fc66be8-4dc3-4626-bb15-3297eb488072", + "metadata": {}, + "outputs": [], + "source": [ + "df.info()" + ] + }, + { + "cell_type": "code", + "execution_count": 19, + "id": "c7d80597-1e62-4d09-ab3c-56ffffa3fb1d", + "metadata": {}, + "outputs": [], + "source": [ + "print(df['Social drinker'].nunique())\n", + "print(df['Social smoker'].nunique())" + ] + }, + { + "cell_type": "code", + "execution_count": 20, + "id": "d03cf0a5-8863-48a0-94fe-87733802d567", + "metadata": {}, + "outputs": [], + "source": [ + "df['Social drinker'] = np.where(df['Social drinker'] == '1' , 1.0 ,0.0)\n", + "df['Social smoker'] = np.where(df['Social smoker'] == '1' , 1.0 ,0.0)" + ] + }, + { + "cell_type": "code", + "execution_count": 21, + "id": "c0378e35-730a-4d2b-97ae-a63f6944f7b4", + "metadata": {}, + "outputs": [], + "source": [ + "df.head()" + ] + }, + { + "cell_type": "code", + "execution_count": 22, + "id": "0b70445e-09f1-432e-b432-606014572ebc", + "metadata": {}, + "outputs": [], + "source": [ + "df.describe()" + ] + }, + { + "cell_type": "code", + "execution_count": 23, + "id": "94370a53-9522-43af-8d2f-f556b7546cf0", + "metadata": {}, + "outputs": [], + "source": [ + "df.info()" + ] + }, + { + "cell_type": "code", + "execution_count": 24, + "id": "60a7c1de-076b-4cec-86f7-b5f61d3524ee", + "metadata": {}, + "outputs": [], + "source": [ + "df.to_csv(\"absenteeism_at_work_converted.csv\")" + ] + }, + { + "cell_type": "code", + "execution_count": 25, + "id": "c0cfdca5-c51b-4ce5-af2b-18d9ff492182", + "metadata": {}, + "outputs": [], + "source": [ + "df.isnull().sum()" + ] + }, + { + "cell_type": "code", + "execution_count": 26, + "id": "28c662dc-9a7d-4817-8c49-9607992d5b85", + "metadata": {}, + "outputs": [], + "source": [ + "print(df.columns)" + ] + }, + { + "cell_type": "code", + "execution_count": 27, + "id": "727e8426-8eb6-44ed-8618-eebb39413398", + "metadata": {}, + "outputs": [], + "source": [ + "import matplotlib.pyplot as plt\n", + "import seaborn as sns" + ] + }, + { + "cell_type": "code", + "execution_count": 28, + "id": "5eb66d2b-4165-4c8e-ba48-17ede0403cab", + "metadata": {}, + "outputs": [], + "source": [ + "plt.figure(figsize = (15,10))\n", + "sns.countplot(x='Reason for absence', data=df)\n", + "plt.show()" + ] + }, + { + "cell_type": "code", + "execution_count": 29, + "id": "fd1239f5-c9d5-40af-9faa-3dc289d48f17", + "metadata": {}, + "outputs": [], + "source": [ + "fig, axs = plt.subplots(nrows=len(df.columns) // 2 + 1, ncols=2, figsize=(12, 50))\n", + "for i, col in enumerate(df.columns):\n", + " row = i // 2\n", + " col_idx = i % 2\n", + " sns.histplot(x=df[col], kde=True, ax=axs[row, col_idx])\n", + " axs[row, col_idx].set_title(col)\n", + "plt.tight_layout()\n", + "plt.show()" + ] + }, + { + "cell_type": "code", + "execution_count": 30, + "id": "f3270d88-6855-465f-89da-6b566788e197", + "metadata": {}, + "outputs": [], + "source": [ + "plt.figure(figsize = (15,10))\n", + "sns.scatterplot(data = df, x = 'Reason for absence', y = 'Month of absence',size = 'Absenteeism time in hours')\n", + "plt.legend()\n", + "plt.show()" + ] + }, + { + "cell_type": "code", + "execution_count": 31, + "id": "3f119e05-47b4-44c1-a53e-ba73ff498d6a", + "metadata": {}, + "outputs": [], + "source": [ + "plt.scatter(df['Distance from Residence to Work'], df['Transportation expense'], s=df['Absenteeism time in hours'])\n", + "plt.xlabel('Distance from Residence to Work')\n", + "plt.ylabel(\"Transportation expense\")\n", + "plt.show()" + ] + }, + { + "cell_type": "code", + "execution_count": 32, + "id": "1d79c243-c2f8-4ba9-ad85-2968ac5b481c", + "metadata": {}, + "outputs": [], + "source": [ + "plt.scatter(df['Distance from Residence to Work'], df['Transportation expense'], s=df['Seasons'])\n", + "plt.xlabel('Distance from Residence to Work')\n", + "plt.ylabel(\"Transportation expense\")\n", + "plt.show()" + ] + }, + { + "cell_type": "code", + "execution_count": 33, + "id": "44f56703-a053-4763-88b6-dfc8372b3d05", + "metadata": {}, + "outputs": [], + "source": [ + "plt.scatter(df['Day of the week'], df['Service time'], s=df['Absenteeism time in hours'])\n", + "plt.xlabel('Day of the week')\n", + "plt.ylabel(\"Service time\")\n", + "plt.show()" + ] + }, + { + "cell_type": "code", + "execution_count": 34, + "id": "8dce3941-1cde-4ee2-952b-f99ebb3fcffb", + "metadata": {}, + "outputs": [], + "source": [ + "plt.scatter(df['Service time'], df['Work load Average/day'], s=df['Absenteeism time in hours'])\n", + "plt.xlabel(\"Service Time\")\n", + "plt.ylabel('Work load Average/day')\n", + "plt.show()" + ] + }, + { + "cell_type": "code", + "execution_count": 35, + "id": "3828a0cd-788d-455d-a000-47f06ca7b9c5", + "metadata": {}, + "outputs": [], + "source": [ + "plt.scatter(df['Age'], df['Work load Average/day'], s=df['Absenteeism time in hours'])\n", + "plt.xlabel(\"Age\")\n", + "plt.ylabel('Work load Average/day')\n", + "plt.show()" + ] + }, + { + "cell_type": "code", + "execution_count": 36, + "id": "d67d0521-ff01-412d-9f3a-01f34381e761", + "metadata": {}, + "outputs": [], + "source": [ + "plt.scatter(df['Hit target'], df['Work load Average/day'], s=df['Absenteeism time in hours'])\n", + "plt.xlabel(\"Hit target\")\n", + "plt.ylabel('Work load Average/day')\n", + "plt.show()" + ] + }, + { + "cell_type": "code", + "execution_count": 37, + "id": "3d063b7e-08f5-457b-a186-20dfcaf518bf", + "metadata": {}, + "outputs": [], + "source": [ + "plt.scatter(df['Age'], df['Hit target'], s=df['Absenteeism time in hours'])\n", + "plt.xlabel('Age')\n", + "plt.ylabel(\"Hit target\")\n", + "plt.show()" + ] + }, + { + "cell_type": "markdown", + "id": "034568a4-f50d-4906-b7f5-f7f80553bdeb", + "metadata": {}, + "source": [ + "#### Pair plot" + ] + }, + { + "cell_type": "code", + "execution_count": 39, + "id": "f0208389-f920-4998-b57b-e422c533156e", + "metadata": {}, + "outputs": [], + "source": [ + "sns.pairplot(data = df)\n", + "plt.show()" + ] + }, + { + "cell_type": "code", + "execution_count": 40, + "id": "bbfbe810-4164-498e-a8f7-efae71dd9f65", + "metadata": {}, + "outputs": [], + "source": [ + "df.corr()" + ] + }, + { + "cell_type": "markdown", + "id": "2c2ae1fc-4051-416d-9c39-02bf10f0ded7", + "metadata": {}, + "source": [ + "#### Hyperparameter tuning on Complete dataset" + ] + }, + { + "cell_type": "code", + "execution_count": 42, + "id": "0e93aba5-c412-484b-9f00-f09ff5412c87", + "metadata": {}, + "outputs": [], + "source": [ + "\n", + "from sklearn.model_selection import GridSearchCV\n", + "from sklearn.tree import DecisionTreeRegressor\n", + "from sklearn.ensemble import RandomForestRegressor, GradientBoostingRegressor, ExtraTreesRegressor, VotingRegressor\n", + "from sklearn.svm import SVR\n", + "from sklearn.ensemble import BaggingRegressor, AdaBoostRegressor\n", + "from sklearn.neural_network import MLPRegressor\n", + "from sklearn.metrics import make_scorer, r2_score, mean_squared_error\n", + "from sklearn.model_selection import train_test_split\n", + "from sklearn.linear_model import Ridge\n", + "from sklearn.kernel_approximation import RBFSampler\n", + "from sklearn.model_selection import train_test_split\n", + "X_train, X_test, Y_train, Y_test= train_test_split(df.drop(columns = 'Absenteeism time in hours'), df['Absenteeism time in hours'], test_size=0.2, random_state=4)\n", + "\n", + "r2_scorer = make_scorer(r2_score, greater_is_better=True)\n", + "mse_scorer = make_scorer(mean_squared_error, greater_is_better=False)\n", + "models = {\n", + " 'ridge': Ridge(),\n", + " 'decision_tree': DecisionTreeRegressor(),\n", + " 'random_forest': RandomForestRegressor(),\n", + " 'extra_trees': ExtraTreesRegressor(),\n", + " 'gradient_boosting': GradientBoostingRegressor(),\n", + " 'mlp': MLPRegressor(),\n", + " 'bagging': BaggingRegressor(),\n", + " 'adaboost': AdaBoostRegressor(),\n", + " 'svr': SVR(),\n", + " 'vote': VotingRegressor(estimators=[\n", + " ('ridge', Ridge()),\n", + " ('decision_tree', DecisionTreeRegressor()),\n", + " ('random_forest', RandomForestRegressor()),\n", + " ('extra_trees', ExtraTreesRegressor()),\n", + " ('gradient_boosting', GradientBoostingRegressor()),\n", + " ('mlp', MLPRegressor()),\n", + " ('bagging', BaggingRegressor()),\n", + " ('adaboost', AdaBoostRegressor()),\n", + " ('svr', SVR())\n", + "])\n", + "}\n", + "from sklearn.model_selection import cross_val_score\n", + "\n", + "def cross_val_scores(model_name, model):\n", + " print(f\"Running cross-validation for {model_name}...\")\n", + " scores_r2 = cross_val_score(model, X_train, Y_train, cv=5, scoring=r2_scorer)\n", + " scores_mse = cross_val_score(model, X_train, Y_train, cv=5, scoring=mse_scorer)\n", + " print(f\"Cross-validation R2 scores for {model_name}: {scores_r2}\")\n", + " print(f\"Cross-validation MSE scores for {model_name}: {scores_mse}\")\n", + " print(f\"Average cross-validation R2 score for {model_name}: {scores_r2.mean()}\")\n", + " print(f\"Average cross-validation MSE score for {model_name}: {scores_mse.mean()}\")\n", + "\n", + "for model_name, model in models.items():\n", + " cross_val_scores(model_name, model)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "2a299f43-2f07-4121-99e4-3cba2c47ffac", + "metadata": {}, + "outputs": [], + "source": [ + "# Import necessary libraries\n", + "from sklearn.model_selection import GridSearchCV\n", + "from sklearn.tree import DecisionTreeRegressor\n", + "from sklearn.ensemble import RandomForestRegressor, GradientBoostingRegressor, ExtraTreesRegressor, VotingRegressor\n", + "from sklearn.svm import SVR\n", + "from sklearn.ensemble import BaggingRegressor, AdaBoostRegressor\n", + "from sklearn.neural_network import MLPRegressor\n", + "from sklearn.metrics import make_scorer, r2_score, mean_squared_error\n", + "from sklearn.model_selection import train_test_split\n", + "from sklearn.linear_model import Ridge\n", + "from sklearn.kernel_approximation import RBFSampler\n", + "\n", + "X_train, X_test, Y_train, Y_test= train_test_split(df.drop(columns = 'Absenteeism time in hours'), df['Absenteeism time in hours'], test_size=0.2, random_state=4)\n", + "\n", + "\n", + "r2_scorer = make_scorer(r2_score, greater_is_better=True)\n", + "mse_scorer = make_scorer(mean_squared_error, greater_is_better=False)\n", + "\n", + "param_grid = {\n", + " 'ridge': {\n", + " 'alpha': [0.01, 0.1, 1.0],\n", + " 'solver': ['auto', 'svd'],\n", + " 'max_iter': [500, 1000]\n", + " },\n", + " 'decision_tree': {\n", + " 'max_depth': [5, 10, 15],\n", + " 'min_samples_split': [2, 5],\n", + " 'min_samples_leaf': [1, 2],\n", + " 'criterion': ['absolute_error', 'squared_error']\n", + " },\n", + " 'random_forest': {\n", + " 'n_estimators': [100, 500, 1000],\n", + " 'max_depth': [5, 10, 15],\n", + " 'min_samples_split': [2, 5],\n", + " 'min_samples_leaf': [1, 2],\n", + " 'criterion': ['absolute_error', 'squared_error']\n", + " },\n", + " 'extra_trees': {\n", + " 'n_estimators': [100, 500, 1000],\n", + " 'max_depth': [5, 10, 15],\n", + " 'min_samples_split': [2, 5],\n", + " 'min_samples_leaf': [1, 2],\n", + " 'criterion': ['absolute_error', 'squared_error']\n", + " },\n", + " 'gradient_boosting': {\n", + " 'n_estimators': [100, 500, 1000],\n", + " 'learning_rate': [0.01, 0.1],\n", + " 'max_depth': [3, 5, 7],\n", + " 'min_samples_leaf': [1, 2],\n", + " 'subsample': [0.8, 0.9]\n", + " },\n", + " 'svr': {\n", + " 'kernel': ['rbf', 'linear'],\n", + " 'C': [1e0, 1e2, 1e4],\n", + " 'epsilon': [0.1, 1.0]\n", + " },\n", + " 'mlp': {\n", + " 'hidden_layer_sizes': [(50, 50), (100, 100)],\n", + " 'activation': ['relu', 'tanh'],\n", + " 'solver': ['adam', 'sgd'],\n", + " 'alpha': [0.001, 0.01],\n", + " 'max_iter': [500, 1000]\n", + " },\n", + " 'bagging': {\n", + " 'n_estimators': [100, 500, 1000],\n", + " 'max_samples': [0.5, 1.0],\n", + " 'max_features': [0.5, 1.0]\n", + " },\n", + " 'adaboost': {\n", + " 'n_estimators': [100, 500, 1000],\n", + " 'learning_rate': [0.01, 0.1]\n", + " },\n", + " 'vote':{\n", + " 'estimators': [100, 500, 1000]\n", + " }\n", + "}\n", + "models = {\n", + " 'ridge': Ridge(),\n", + " 'decision_tree': DecisionTreeRegressor(),\n", + " 'extra_trees': ExtraTreesRegressor(),\n", + " 'gradient_boosting': GradientBoostingRegressor(),\n", + " 'mlp': MLPRegressor(),\n", + " 'bagging': BaggingRegressor(),\n", + " 'adaboost': AdaBoostRegressor(),\n", + " 'svr': SVR(),\n", + " 'random_forest': RandomForestRegressor(),\n", + " 'vote': VotingRegressor(estimators=[\n", + " ('ridge', Ridge()),\n", + " ('decision_tree', DecisionTreeRegressor()),\n", + " ('random_forest', RandomForestRegressor()),\n", + " ('extra_trees', ExtraTreesRegressor()),\n", + " ('gradient_boosting', GradientBoostingRegressor()),\n", + " ('mlp', MLPRegressor()),\n", + " ('bagging', BaggingRegressor()),\n", + " ('adaboost', AdaBoostRegressor()),\n", + " ('svr', SVR())\n", + "])\n", + "}\n", + "\n", + "def grid_search_model(model_name, model, param_grid):\n", + " print(f\"Running GridSearchCV for {model_name}...\")\n", + " scoring = {'r2': r2_scorer, 'mse': mse_scorer}\n", + " grid_search = GridSearchCV(estimator=model, param_grid=param_grid, scoring=scoring, refit='r2', cv=5, n_jobs=-1)\n", + " grid_search.fit(X_train, Y_train)\n", + " print(f\"Best parameters for {model_name}: {grid_search.best_params_}\")\n", + " print(f\"Best R2 Score for {model_name}: {grid_search.best_score_}\")\n", + " print(f\"Best MSE for {model_name}: {grid_search.cv_results_['mean_test_mse'][grid_search.best_index_]}\")\n", + " return grid_search.best_estimator_\n", + " \n", + "best_models = {}\n", + "for model_name in models:\n", + " best_models[model_name] = grid_search_model(model_name, models[model_name], param_grid[model_name])\n", + "\n", + "\n", + "for model_name, model in best_models.items():\n", + " Y_pred = model.predict(X_test)\n", + " r2 = r2_score(Y_test, Y_pred)\n", + " mse_val = mse(Y_test, Y_pred)\n", + " print(f\"R2 Score for {model_name} on test set: {r2}\")\n", + " print(f\"MSE for {model_name} on test set: {mse_val}\")" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "02088f8b-b7c1-4994-9075-b3b20f0bcd52", + "metadata": {}, + "outputs": [], + "source": [ + "from sklearn.model_selection import RandomizedSearchCV\n", + "from sklearn.tree import DecisionTreeRegressor\n", + "from sklearn.ensemble import RandomForestRegressor, GradientBoostingRegressor, ExtraTreesRegressor, BaggingRegressor, AdaBoostRegressor\n", + "from sklearn.svm import SVR\n", + "from sklearn.neural_network import MLPRegressor\n", + "from sklearn.metrics import make_scorer, r2_score\n", + "from sklearn.model_selection import train_test_split\n", + "from sklearn.linear_model import Ridge\n", + "from sklearn.kernel_approximation import RBFSampler\n", + "\n", + "X_train, X_test, Y_train, Y_test= train_test_split(df.drop(columns = 'Absenteeism time in hours'), df['Absenteeism time in hours'], test_size=0.2, random_state=4)\n", + "\n", + "scorer = make_scorer(r2_score)\n", + "\n", + "param_grid = {\n", + " 'ridge': {\n", + " 'alpha': [0.01, 0.1, 1.0],\n", + " 'solver': ['auto', 'svd'],\n", + " 'max_iter': [500, 1000]\n", + " },\n", + " 'decision_tree': {\n", + " 'max_depth': [5, 10, 15],\n", + " 'min_samples_split': [2, 5],\n", + " 'min_samples_leaf': [1, 2],\n", + " 'criterion': ['absolute_error', 'squared_error']\n", + " },\n", + " 'random_forest': {\n", + " 'n_estimators': [100, 500, 1000],\n", + " 'max_depth': [5, 10, 15],\n", + " 'min_samples_split': [2, 5],\n", + " 'min_samples_leaf': [1, 2],\n", + " 'criterion': ['absolute_error', 'squared_error']\n", + " },\n", + " 'extra_trees': {\n", + " 'n_estimators': [100, 500, 1000],\n", + " 'max_depth': [5, 10, 15],\n", + " 'min_samples_split': [2, 5],\n", + " 'min_samples_leaf': [1, 2],\n", + " 'criterion': ['absolute_error', 'squared_error']\n", + " },\n", + " 'gradient_boosting': {\n", + " 'n_estimators': [100, 500, 1000],\n", + " 'learning_rate': [0.01, 0.1],\n", + " 'max_depth': [3, 5, 7],\n", + " 'min_samples_leaf': [1, 2],\n", + " 'subsample': [0.8, 0.9]\n", + " },\n", + " 'svr': {\n", + " 'kernel': ['rbf', 'linear'],\n", + " 'C': [1e0, 1e2, 1e4],\n", + " 'epsilon': [0.1, 1.0]\n", + " },\n", + " 'mlp': {\n", + " 'hidden_layer_sizes': [(50, 50), (100, 100)],\n", + " 'activation': ['relu', 'tanh'],\n", + " 'solver': ['adam', 'sgd'],\n", + " 'alpha': [0.001, 0.01],\n", + " 'max_iter': [500, 1000]\n", + " },\n", + " 'bagging': {\n", + " 'n_estimators': [100, 500, 1000],\n", + " 'max_samples': [0.5, 1.0],\n", + " 'max_features': [0.5, 1.0]\n", + " },\n", + " 'adaboost': {\n", + " 'n_estimators': [100, 500, 1000],\n", + " 'learning_rate': [0.01, 0.1]\n", + " },\n", + " 'vote':{\n", + " 'estimators': [100, 500, 1000]\n", + " }\n", + "}\n", + "models = {\n", + " 'ridge': Ridge(),\n", + " 'decision_tree': DecisionTreeRegressor(),\n", + " 'random_forest': RandomForestRegressor(),\n", + " 'extra_trees': ExtraTreesRegressor(),\n", + " 'gradient_boosting': GradientBoostingRegressor(),\n", + " 'mlp': MLPRegressor(),\n", + " 'bagging': BaggingRegressor(),\n", + " 'adaboost': AdaBoostRegressor(),\n", + " 'svr': SVR(),\n", + "}\n", + "\n", + "def random_search_model(model_name, model, param_distribution):\n", + " print(f\"Running RandomizedSearchCV for {model_name}...\")\n", + " random_search = RandomizedSearchCV(estimator=model, param_distributions=param_distribution, cv=5, n_iter=10, random_state=42, scoring=scorer)\n", + " random_search.fit(X_train, Y_train)\n", + " print(f\"Best parameters for {model_name}: {random_search.best_params_}\")\n", + " print(f\"Best R2 Score for {model_name}: {random_search.best_score_}\")\n", + " return random_search.best_estimator_\n", + "\n", + "for model_name in models:\n", + " best_models[model_name] = random_search_model(model_name, models[model_name], param_distributions[model_name])\n", + "\n", + "for model_name, model in best_models.items():\n", + " Y_pred = model.predict(X_test)\n", + " r2 = r2_score(Y_test, Y_pred)\n", + " print(f\"R2 Score for {model_name} on test set: {r2}\")\n", + "best_model_name = max(best_models, key=lambda x: r2_score(Y_test, best_models[x].predict(X_test)))\n", + "print(f\"Best model: {best_model_name}\")\n", + "\n", + "Y_pred = best_models[best_model_name].predict(X_test)\n", + "\n", + "r2 = r2_score(Y_test, Y_pred)\n", + "print(f\"R2 Score for best model on test set: {r2}\")" + ] + }, + { + "cell_type": "markdown", + "id": "ab8bb1f3-2b16-4639-b6c2-2e25249e981e", + "metadata": {}, + "source": [ + "#### PCA Analysis on Dataset" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "7363467d-a4f6-42de-86e5-5edf329b0422", + "metadata": {}, + "outputs": [], + "source": [ + "from sklearn.decomposition import PCA\n", + "pca = PCA(n_components=5)\n", + "\n", + "# Fit the PCA object to your data and transform it\n", + "pca_df = pca.fit_transform(df.drop(columns = 'Absenteeism time in hours'))\n", + "\n", + "# Convert the transformed data back into a pandas DataFrame\n", + "pca_df = pd.DataFrame(pca_df, columns=['PC1', 'PC2','PC3','PC4','PC5'])\n", + "\n", + "# Print the explained variance ratio for each component\n", + "print(pca.explained_variance_ratio_)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "6160d3df-a08d-4d63-baba-c320806a9754", + "metadata": {}, + "outputs": [], + "source": [ + "print(pca.components_)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "2bfc1c28-e866-47f1-8a18-6dc6d507da25", + "metadata": {}, + "outputs": [], + "source": [ + "from sklearn.linear_model import Lasso\n", + "from sklearn.model_selection import train_test_split\n", + "X_train, X_test, y_train, y_test = train_test_split(pca_df[['PC1', 'PC2', 'PC3']], df['Absenteeism time in hours'], test_size=0.2, random_state=42)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "b22691a7-073f-44c8-b377-6845ed20cea5", + "metadata": {}, + "outputs": [], + "source": [ + "from mpl_toolkits.mplot3d import Axes3D\n", + "\n", + "# Create a 3D scatter plot of the first three principal components\n", + "fig = plt.figure()\n", + "ax = fig.add_subplot(111, projection='3d')\n", + "ax.scatter(pca_df['PC1'], pca_df['PC2'], pca_df['PC3'], c = df['Absenteeism time in hours'])\n", + "ax.set_xlabel('PC1')\n", + "ax.set_ylabel('PC2')\n", + "ax.set_zlabel('PC3')\n", + "ax.set_title('3D Scatter Plot of First Three Principal Components')\n", + "plt.show()" + ] + }, + { + "cell_type": "markdown", + "id": "fcad4b94-02e0-4405-98ae-a3fee831d26f", + "metadata": {}, + "source": [ + "#### Hyperparameter tuning on PCA Dataset" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "57cfcdcd-af18-4553-babe-e5f25a4a2c11", + "metadata": {}, + "outputs": [], + "source": [ + "from sklearn.model_selection import train_test_split\n", + "X_train, X_test, Y_train, Y_test= train_test_split(df.drop(columns = 'Absenteeism time in hours'), df['Absenteeism time in hours'], test_size=0.2, random_state=4)\n", + "\n", + "models = {\n", + " 'ridge': Ridge(),\n", + " 'decision_tree': DecisionTreeRegressor(),\n", + " 'random_forest': RandomForestRegressor(),\n", + " 'extra_trees': ExtraTreesRegressor(),\n", + " 'gradient_boosting': GradientBoostingRegressor(),\n", + " 'mlp': MLPRegressor(),\n", + " 'bagging': BaggingRegressor(),\n", + " 'adaboost': AdaBoostRegressor(),\n", + " 'svr': SVR(),\n", + " 'vote': VotingRegressor(estimators=[\n", + " ('ridge', Ridge()),\n", + " ('decision_tree', DecisionTreeRegressor()),\n", + " ('random_forest', RandomForestRegressor()),\n", + " ('extra_trees', ExtraTreesRegressor()),\n", + " ('gradient_boosting', GradientBoostingRegressor()),\n", + " ('mlp', MLPRegressor()),\n", + " ('bagging', BaggingRegressor()),\n", + " ('adaboost', AdaBoostRegressor()),\n", + " ('svr', SVR())\n", + "])\n", + "}\n", + "r2_scorer = make_scorer(r2_score, greater_is_better=True)\n", + "mse_scorer = make_scorer(mean_squared_error, greater_is_better=False)\n", + "from sklearn.model_selection import cross_val_score\n", + "\n", + "def cross_val_scores(model_name, model):\n", + " print(f\"Running cross-validation for {model_name}...\")\n", + " scores_r2 = cross_val_score(model, X_train, Y_train, cv=5, scoring=r2_scorer)\n", + " scores_mse = cross_val_score(model, X_train, Y_train, cv=5, scoring=mse_scorer)\n", + " print(f\"Cross-validation R2 scores for {model_name}: {scores_r2}\")\n", + " print(f\"Cross-validation MSE scores for {model_name}: {scores_mse}\")\n", + " print(f\"Average cross-validation R2 score for {model_name}: {scores_r2.mean()}\")\n", + " print(f\"Average cross-validation MSE score for {model_name}: {scores_mse.mean()}\")\n", + "\n", + "for model_name, model in models.items():\n", + " cross_val_scores(model_name, model)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "5befe8a3-40b9-49ba-9ac4-2b11b3531e98", + "metadata": {}, + "outputs": [], + "source": [ + "from sklearn.decomposition import PCA\n", + "from sklearn.model_selection import GridSearchCV\n", + "\n", + "pca = PCA()\n", + "\n", + "param_grid = {\n", + " 'n_components': [2, 3, 4, 5, 6, 7, 8, 9, 10]\n", + "}\n", + "grid_search = GridSearchCV(estimator=pca, param_grid=param_grid, cv=5)\n", + "grid_search.fit(df.drop(columns = 'Absenteeism time in hours'))\n", + "\n", + "print(\"Best hyperparameters:\", grid_search.best_params_)\n", + "print(\"Best score:\", grid_search.best_score_)\n", + "\n", + "best_pca = grid_search.best_estimator_\n", + "best_pca_df = best_pca.fit_transform(df.drop(columns = 'Absenteeism time in hours'))\n", + "\n", + "# Use the n_components attribute to determine the number of columns\n", + "n_components = best_pca.n_components_\n", + "columns = [f'PC{i+1}' for i in range(n_components)]\n", + "\n", + "best_pca_df = pd.DataFrame(best_pca_df, columns=columns)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "3ebdb33b-8980-47fd-bb9e-aa72d0708dc5", + "metadata": {}, + "outputs": [], + "source": [ + "from sklearn.decomposition import PCA\n", + "from sklearn.model_selection import RandomizedSearchCV\n", + "import pandas as pd\n", + "import numpy as np\n", + "\n", + "# Assuming df is your DataFrame\n", + "pca = PCA()\n", + "\n", + "param_distribution = {\n", + " 'n_components': [2, 3, 4, 5, 6, 7, 8, 9, 10]\n", + "}\n", + "random_search = RandomizedSearchCV(estimator=pca, param_distributions=param_distribution, cv=5, n_iter=10, random_state=42)\n", + "random_search.fit(df.drop(columns = 'Absenteeism time in hours'))\n", + "\n", + "print(\"Best hyperparameters:\", random_search.best_params_)\n", + "print(\"Best score:\", random_search.best_score_)\n", + "\n", + "best_pca = random_search.best_estimator_\n", + "\n", + "best_pca_df = pd.DataFrame(best_pca.fit_transform(df.drop(columns = 'Absenteeism time in hours')), \n", + " columns=[f'PC{i+1}' for i in range(best_pca.n_components_)])" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "caa337f4-33fd-406c-9e67-1d5f5a50bb0d", + "metadata": {}, + "outputs": [], + "source": [ + "# Import necessary libraries\n", + "from sklearn.model_selection import RandomizedSearchCV\n", + "from sklearn.tree import DecisionTreeRegressor\n", + "from sklearn.ensemble import RandomForestRegressor, GradientBoostingRegressor, ExtraTreesRegressor, BaggingRegressor, AdaBoostRegressor\n", + "from sklearn.svm import SVR\n", + "from sklearn.neural_network import MLPRegressor\n", + "from sklearn.metrics import make_scorer, r2_score\n", + "from sklearn.model_selection import train_test_split\n", + "from sklearn.linear_model import Ridge\n", + "from sklearn.kernel_approximation import RBFSampler\n", + "\n", + "X = df.drop(columns='Absenteeism time in hours')\n", + "Y = df['Absenteeism time in hours']\n", + "X_train, X_test, Y_train, Y_test = train_test_split(X, Y, test_size=0.2, random_state=4)\n", + "\n", + "X_train_pca = pca.fit_transform(X_train)\n", + "X_test_pca = pca.transform(X_test)\n", + "\n", + "# Define R2 scorer for RandomizedSearchCV\n", + "scorer = make_scorer(r2_score)\n", + "# Define hyperparameter distributions for each model\n", + "param_distributions = {\n", + " 'ridge': {\n", + " 'alpha': [0.05, 0.01, 0.05, 0.1, 0.5, 1.0, 5.0, 10.0],\n", + " 'solver': ['svd', 'cholesky']\n", + " },\n", + " 'decision_tree': {\n", + " 'max_depth': [3, 5, 7, 10, 15, 20, 25],\n", + " 'min_samples_split': [2, 5, 7, 10],\n", + " 'min_samples_leaf': [1, 2, 4, 5],\n", + " 'criterion': ['absolute_error', 'poisson', 'squared_error', 'friedman_mse']\n", + " },\n", + " 'random_forest': {\n", + " 'n_estimators': [100, 250, 500, 1000, 2000, 5000, 10000],\n", + " 'max_depth': [5, 7, 10, 15, 20, 25],\n", + " 'min_samples_split': [2, 5, 7, 10],\n", + " 'min_samples_leaf': [1, 2, 4, 5],\n", + " 'criterion': ['absolute_error', 'poisson', 'squared_error', 'friedman_mse']\n", + " },\n", + " 'extra_trees': {\n", + " 'n_estimators': [100, 250, 500, 1000, 2000, 5000, 10000],\n", + " 'max_depth': [5, 7, 10, 15, 20, 25],\n", + " 'min_samples_split': [2, 5, 7, 10],\n", + " 'min_samples_leaf': [1, 2, 4, 5],\n", + " 'criterion': ['absolute_error', 'poisson', 'squared_error', 'friedman_mse']\n", + " },\n", + " 'gradient_boosting': {\n", + " 'n_estimators': [100, 250, 500, 1000, 2000, 5000, 10000],\n", + " 'learning_rate': [0.001, 0.005, 0.01, 0.05, 0.1],\n", + " 'max_depth': [3, 5, 7, 10, 15, 20, 25],\n", + " 'subsample': [0.8, 0.9, 1.0]\n", + " },\n", + " 'svr': {\n", + " 'kernel': ['poly', 'sigmoid', 'rbf', 'linear'],\n", + " 'C': [1e0, 1e2, 1e4, 1e6, 1e8, 1e10],\n", + " 'epsilon': [0.1, 0.5, 1.0, 5.0, 10.0]\n", + " },\n", + " 'mlp': {\n", + " 'hidden_layer_sizes': [(50, 50), (100, 100), (200, 200), (500, 500), (1000, 1000)],\n", + " 'activation': ['relu', 'tanh', 'sigmoid'],\n", + " 'solver': ['adam', 'sgd'],\n", + " 'alpha': [0.001, 0.01, 0.05, 0.1, 0.5, 1.0]\n", + " },\n", + " 'bagging': {\n", + " 'n_estimators': [100, 250, 500, 1000, 2000, 5000, 10000],\n", + " 'max_samples': [0.2, 0.5, 0.8, 1.0],\n", + " 'max_features': [0.2, 0.5, 0.8, 1.0]\n", + " },\n", + " 'adaboost': {\n", + " 'n_estimators': [100, 250, 500, 1000, 2000, 5000, 10000],\n", + " 'learning_rate': [0.001, 0.01, 0.05, 0.1]\n", + " }\n", + "}\n", + "# Define models with their parameter distributions\n", + "models = {\n", + " 'ridge': Ridge(),\n", + " 'decision_tree': DecisionTreeRegressor(),\n", + " 'random_forest': RandomForestRegressor(),\n", + " 'extra_trees': ExtraTreesRegressor(),\n", + " 'gradient_boosting': GradientBoostingRegressor(),\n", + " 'mlp': MLPRegressor(),\n", + " 'bagging': BaggingRegressor(),\n", + " 'adaboost': AdaBoostRegressor(),\n", + " 'svr': SVR(),\n", + "}\n", + "\n", + "# Function to perform random search and get the best model for each regression\n", + "def random_search_model(model_name, model, param_distribution):\n", + " print(f\"Running RandomizedSearchCV for {model_name}...\")\n", + " random_search = RandomizedSearchCV(estimator=model, param_distributions=param_distribution, cv=5, n_iter=10, random_state=42, scoring=scorer)\n", + " random_search.fit(X_train, Y_train)\n", + " print(f\"Best parameters for {model_name}: {random_search.best_params_}\")\n", + " print(f\"Best R2 Score for {model_name}: {random_search.best_score_}\")\n", + " return random_search.best_estimator_\n", + "\n", + "# Running RandomizedSearchCV for each model\n", + "best_models = {}\n", + "for model_name in models:\n", + " best_models[model_name] = random_search_model(model_name, models[model_name], param_distributions[model_name])\n", + "\n", + "# Evaluate the best models on the test set\n", + "for model_name, model in best_models.items():\n", + " Y_pred = model.predict(X_test)\n", + " r2 = r2_score(Y_test, Y_pred)\n", + " print(f\"R2 Score for {model_name} on test set: {r2}\")\n", + "\n", + "# Compare the performance of the best models\n", + "best_model_name = max(best_models, key=lambda x: r2_score(Y_test, best_models[x].predict(X_test)))\n", + "print(f\"Best model: {best_model_name}\")\n", + "\n", + "# Use the best model to make predictions on the test set\n", + "Y_pred = best_models[best_model_name].predict(X_test)\n", + "\n", + "# Evaluate the performance of the best model\n", + "r2 = r2_score(Y_test, Y_pred)\n", + "print(f\"R2 Score for best model on test set: {r2}\")\n", + "\n", + "# Plot the predicted values against the actual values\n", + "import matplotlib.pyplot as plt\n", + "plt.scatter(Y_test, Y_pred)\n", + "plt.xlabel(\"Actual values\")\n", + "plt.ylabel(\"Predicted values\")\n", + "plt.show()" + ] + }, + { + "cell_type": "markdown", + "id": "217fe4f9-a1af-48a1-a193-c71b5f77f42c", + "metadata": {}, + "source": [ + "#### Normalization on Dataset" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "f4fe3e99-8c9d-457b-9797-ebd77da623fd", + "metadata": {}, + "outputs": [], + "source": [ + "from sklearn.preprocessing import MinMaxScaler\n", + "df_1 = df.copy()\n", + "df_1p = df_1.drop(columns = 'Absenteeism time in hours')\n", + "normalizer = MinMaxScaler()\n", + "normalized_data = normalizer.fit_transform(df_1p)\n", + "\n", + "\n", + "normalized_df = pd.DataFrame(normalized_data, columns=df_1p.columns)\n", + "print(\"Standardized Data:\")\n", + "normalized_df.head()" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "15b567e2-c079-453c-bfc1-68ff08461f45", + "metadata": {}, + "outputs": [], + "source": [ + "normalized_df['Absenteeism time in hours'] = df['Absenteeism time in hours']" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "260c0e2b-94a4-450e-ba02-deb5ad92d36f", + "metadata": {}, + "outputs": [], + "source": [ + "normalized_df.head()" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "a386d980-f2b5-4a52-9780-c1ede629c9d2", + "metadata": {}, + "outputs": [], + "source": [ + "from sklearn.model_selection import train_test_split\n", + "X_train, X_test, Y_train, Y_test= train_test_split(normalized_df.drop(columns = 'Absenteeism time in hours'), normalized_df['Absenteeism time in hours'], test_size=0.2, random_state=4)\n", + "\n", + "\n", + "from sklearn.model_selection import cross_val_score\n", + "\n", + "def cross_val_scores(model_name, model):\n", + " print(f\"Running cross-validation for {model_name}...\")\n", + " scores_r2 = cross_val_score(model, X_train, Y_train, cv=5, scoring=r2_scorer)\n", + " scores_mse = cross_val_score(model, X_train, Y_train, cv=5, scoring=mse_scorer)\n", + " print(f\"Cross-validation R2 scores for {model_name}: {scores_r2}\")\n", + " print(f\"Cross-validation MSE scores for {model_name}: {scores_mse}\")\n", + " print(f\"Average cross-validation R2 score for {model_name}: {scores_r2.mean()}\")\n", + " print(f\"Average cross-validation MSE score for {model_name}: {scores_mse.mean()}\")\n", + "\n", + "for model_name, model in models.items():\n", + " cross_val_scores(model_name, model)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "f42a1aca-a178-40f9-be6e-303c1ccde733", + "metadata": {}, + "outputs": [], + "source": [] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "d1a47fc5-8e9a-4bc3-9cf8-ce4e3540370c", + "metadata": {}, + "outputs": [], + "source": [] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "e9a49311-b5ec-471a-99e2-673fb6738157", + "metadata": {}, + "outputs": [], + "source": [ + "from sklearn.feature_selection import SelectFromModel\n", + "\n", + "lasso = Lasso(alpha=0.01)\n", + "lasso.fit(X = df.drop(columns = 'Absenteeism time in hours'), y = df['Absenteeism time in hours'])\n", + "coefficients = lasso.coef_\n", + "\n", + "selection = SelectFromModel(lasso, prefit=True)\n", + "X_selected = selection.transform(df.drop(columns='Absenteeism time in hours'))\n", + "\n", + "feature_names = df.drop(columns='Absenteeism time in hours').columns\n", + "\n", + "print(\"Selected features:\", feature_names[selection.get_support()])\n", + "print(\"Coefficients:\", coefficients[selection.get_support()])\n" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "7cd073d6-7f97-4155-8407-1c1e2b9f25ba", + "metadata": {}, + "outputs": [], + "source": [ + "from sklearn.inspection import permutation_importance\n", + "\n", + "# Create a Lasso regression model with a regularization parameter of 0.01\n", + "lasso = Lasso(alpha=0.01)\n", + "\n", + "# Fit the Lasso model to the data\n", + "lasso.fit(X=df.drop(columns='Absenteeism time in hours'), y=df['Absenteeism time in hours'])\n", + "\n", + "# Get the permutation importance of the features\n", + "importances = permutation_importance(lasso, df.drop(columns='Absenteeism time in hours'), df['Absenteeism time in hours'], n_repeats=10, random_state=42)\n", + "\n", + "# Print the feature importances\n", + "print(\"Feature importances:\", importances.importances_mean)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "5433622a-53bb-4554-91e1-399fea5a9605", + "metadata": {}, + "outputs": [], + "source": [ + "plt.bar(range(len(importances.importances_mean)), importances.importances_mean)\n", + "plt.xlabel('Feature Index')\n", + "plt.ylabel('Importance')\n", + "plt.title('Feature Importances')\n", + "plt.show()" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "238845c7-550a-44a7-99f0-f7e8f2c76710", + "metadata": {}, + "outputs": [], + "source": [ + "feature_names = df.drop(columns='Absenteeism time in hours').columns\n", + "\n", + "top_features_indices = np.argsort(importances.importances_mean)[::-1][:5]\n", + "top_features = feature_names[top_features_indices]\n", + "print(\"Top 5 most important features:\", top_features)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "254f6b7f-fbbf-42e2-a23d-a738811b6923", + "metadata": {}, + "outputs": [], + "source": [ + "X_imp = df[top_features]" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "0e38e186-6469-4f15-90b9-46479757a06e", + "metadata": {}, + "outputs": [], + "source": [ + "X_imp.head()" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "804513b6-d315-40a7-bfb3-4e9ab165a0eb", + "metadata": {}, + "outputs": [], + "source": [ + "Y_imp = df['Absenteeism time in hours']\n", + "Y_imp.head()" + ] + }, + { + "cell_type": "markdown", + "id": "22591b7c-dc3f-48fd-a213-d1a34d084758", + "metadata": {}, + "source": [ + "#### CV score for top selected features\n", + "\n", + "\n", + "\n" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "a6a7494c-3993-471f-9d17-ce9bf616901b", + "metadata": {}, + "outputs": [], + "source": [ + "from sklearn.model_selection import train_test_split\n", + "X_train, X_test, Y_train, Y_test= train_test_split(X_imp,Y_imp, test_size=0.2, random_state=4)\n", + "\n", + "\n", + "from sklearn.model_selection import cross_val_score\n", + "\n", + "def cross_val_scores(model_name, model):\n", + " print(f\"Running cross-validation for {model_name}...\")\n", + " scores_r2 = cross_val_score(model, X_train, Y_train, cv=5, scoring=r2_scorer)\n", + " scores_mse = cross_val_score(model, X_train, Y_train, cv=5, scoring=mse_scorer)\n", + " print(f\"Cross-validation R2 scores for {model_name}: {scores_r2}\")\n", + " print(f\"Cross-validation MSE scores for {model_name}: {scores_mse}\")\n", + " print(f\"Average cross-validation R2 score for {model_name}: {scores_r2.mean()}\")\n", + " print(f\"Average cross-validation MSE score for {model_name}: {scores_mse.mean()}\")\n", + "\n", + "for model_name, model in models.items():\n", + " cross_val_scores(model_name, model)" + ] + }, + { + "cell_type": "markdown", + "id": "d195c5d8-6bd1-4952-aac6-a8569db864a3", + "metadata": {}, + "source": [ + "## Data Restructuring" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "de0d242e-fd1e-4424-8835-59fe71e1fb32", + "metadata": {}, + "outputs": [], + "source": [ + "allabsents=df[df['Absenteeism time in hours']!=0]\n", + "allabsents=allabsents.drop(['Disciplinary failure'],axis=1)\n", + "len(allabsents.ID.unique())" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "2a96ece3-6038-416f-869c-ce3a53c287fa", + "metadata": {}, + "outputs": [], + "source": [ + "target=allabsents.groupby(['ID'])['Hit target'].mean()\n", + "work=allabsents.groupby(['ID'])['Work load Average/day'].mean()\n", + "age=allabsents.groupby(['ID'])['Age'].mean()\n", + "transport=allabsents.groupby(['ID'])['Transportation expense'].mean()\n", + "height=allabsents.groupby(['ID'])['Height'].mean()\n", + "service=allabsents.groupby(['ID'])['Service time'].mean()\n", + "weight=allabsents.groupby(['ID'])['Weight'].mean()\n", + "body=allabsents.groupby(['ID'])['Body mass index'].mean()\n", + "hours=allabsents.groupby(['ID'])['Absenteeism time in hours'].sum()\n", + "drink=allabsents.groupby(['ID'])['Social drinker'].mean()\n", + "smoker=allabsents.groupby(['ID'])['Social smoker'].mean()\n", + "education=allabsents.groupby(['ID'])['Education'].mean()\n", + "pet=allabsents.groupby(['ID'])['Pet'].mean()\n", + "smoker=allabsents.groupby(['ID'])['Social smoker'].mean()\n", + "distance=allabsents.groupby(['ID'])['Distance from Residence to Work'].mean()\n", + "df_optimised=pd.concat([target,work,age,transport,service,height,body,weight,drink,smoker,education,pet,distance,hours],axis=1)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "3931e3b8-6c41-4e01-8ce2-edeadd18f45e", + "metadata": {}, + "outputs": [], + "source": [ + "def correlation_heatmap(li):\n", + " correlations = li.corr()\n", + "\n", + " fig, ax = plt.subplots(figsize=(9,9))\n", + " sns.heatmap(correlations, vmax=1.0, center=0, fmt='.2f',\n", + " square=True, linewidths=.5, annot=True, cbar_kws={\"shrink\": .70})\n", + " plt.show();\n", + " \n", + "correlation_heatmap(df_optimised[['Age','Weight','Transportation expense' ,'Body mass index','Distance from Residence to Work','Service time','Height','Work load Average/day','Hit target','Absenteeism time in hours']])" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "12bf429e-1af1-4393-891d-2c750ddb3f21", + "metadata": {}, + "outputs": [], + "source": [ + "for i in df.columns:\n", + " sns.boxplot(df[i])\n", + " plt.tight_layout()\n", + " plt.show()" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "4adcda89-78ea-4fc3-9b5c-c510efaee2ec", + "metadata": {}, + "outputs": [], + "source": [ + "plt.figure(figsize = (15,10))\n", + "sns.countplot(x='Absenteeism time in hours',data=df)\n", + "plt.show()" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "92d78281-7352-4d3a-8b92-e89dea2d85e9", + "metadata": {}, + "outputs": [], + "source": [ + "abt=[]\n", + "for i in df['Absenteeism time in hours']:\n", + " if i<8:\n", + " abt.append(0)\n", + " else:\n", + " abt.append(1)\n", + "df['newabsent']=abt" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "c8cd7c38-edea-4e55-9bce-8357ef3a112c", + "metadata": {}, + "outputs": [], + "source": [ + "bo=[]\n", + "for i in df['Reason for absence']:\n", + " if i<20:\n", + " bo.append(1)\n", + " else:\n", + " bo.append(0)\n", + "df['newReason']=bo" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "cc758cb3-7218-41f8-9e61-61e916656116", + "metadata": {}, + "outputs": [], + "source": [ + "col=['Age','Service time','Work load Average/day','Hit target','Weight']\n", + "for i in col:\n", + " sns.lineplot(x=i,y='Absenteeism time in hours',hue='newReason',data=df)\n", + " plt.tight_layout()\n", + " plt.show()" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "6e5c4189-b0ab-431e-aa0e-7b5e58558b74", + "metadata": {}, + "outputs": [], + "source": [ + "col=['Body mass index', 'Weight', 'Disciplinary failure','newReason','Age','Service time','Work load Average/day','Hit target']\n", + "new_df = df[col]" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "3855f36c-f668-4d30-96db-c8ea357a9c83", + "metadata": {}, + "outputs": [], + "source": [ + "new_df.head()" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "20ca0b41-2ee1-49c7-b031-2ac7b18c403c", + "metadata": {}, + "outputs": [], + "source": [ + "X = new_df\n", + "y = df['Absenteeism time in hours']\n", + "\n", + "print(X.head())\n", + "y.head()" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "240acdc8-2f43-4b13-880d-9e444004adeb", + "metadata": {}, + "outputs": [], + "source": [ + "from sklearn.model_selection import train_test_split\n", + "X_train, X_test, Y_train, Y_test= train_test_split(X, y, test_size=0.2, random_state=4)\n", + "\n", + "\n", + "from sklearn.model_selection import cross_val_score\n", + "\n", + "models = {\n", + " 'ridge': Ridge(),\n", + " 'decision_tree': DecisionTreeRegressor(),\n", + " 'random_forest': RandomForestRegressor(),\n", + " 'extra_trees': ExtraTreesRegressor(),\n", + " 'gradient_boosting': GradientBoostingRegressor(),\n", + " 'mlp': MLPRegressor(),\n", + " 'bagging': BaggingRegressor(),\n", + " 'adaboost': AdaBoostRegressor(),\n", + " 'svr': SVR(),\n", + " 'vote': VotingRegressor(estimators=[\n", + " ('ridge', Ridge()),\n", + " ('decision_tree', DecisionTreeRegressor()),\n", + " ('random_forest', RandomForestRegressor()),\n", + " ('extra_trees', ExtraTreesRegressor()),\n", + " ('gradient_boosting', GradientBoostingRegressor()),\n", + " ('mlp', MLPRegressor()),\n", + " ('bagging', BaggingRegressor()),\n", + " ('adaboost', AdaBoostRegressor()),\n", + " ('svr', SVR())\n", + "])\n", + "}\n", + "def cross_val_scores(model_name, model):\n", + " print(f\"Running cross-validation for {model_name}...\")\n", + " scores_r2 = cross_val_score(model, X_train, Y_train, cv=5, scoring=r2_scorer)\n", + " scores_mse = cross_val_score(model, X_train, Y_train, cv=5, scoring=mse_scorer)\n", + " print(f\"Cross-validation R2 scores for {model_name}: {scores_r2}\")\n", + " print(f\"Cross-validation MSE scores for {model_name}: {scores_mse}\")\n", + " print(f\"Average cross-validation R2 score for {model_name}: {scores_r2.mean()}\")\n", + " print(f\"Average cross-validation MSE score for {model_name}: {scores_mse.mean()}\")\n", + "\n", + "for model_name, model in models.items():\n", + " cross_val_scores(model_name, model)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "a5b63bce-832b-46df-aba5-85dea52b21e6", + "metadata": {}, + "outputs": [], + "source": [] + }, + { + "cell_type": "markdown", + "id": "cd11a188-00ef-4b46-aa21-300b372cad32", + "metadata": {}, + "source": [ + "## **Ridge Regression Model**" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "8ccbb78a-1216-4087-a57e-d88a3b9425cd", + "metadata": {}, + "outputs": [], + "source": [ + "from sklearn.linear_model import Ridge\n", + "from sklearn.model_selection import train_test_split\n", + "from sklearn.metrics import mean_squared_error\n", + "\n", + "X = df[['Body mass index', 'Weight', 'Disciplinary failure','newReason','Age','Service time','Work load Average/day','Hit target']]\n", + "y = df['Absenteeism time in hours']\n", + "\n", + "X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=42)\n", + "\n", + "ridge_model = Ridge(alpha = 0.5)\n", + "\n", + "ridge_model.fit(X_train, y_train)\n", + "\n", + "\n", + "y_pred = ridge_model.predict(X_test)\n", + "\n", + "\n", + "mse = mean_squared_error(y_test, y_pred)\n", + "print(\"Mean Squared Error (MSE) of model over Test dataset: \", mse)\n", + "\n", + "input_features = [31.0, 98.0, 1.0, 1, 50, 18.0, 239554, 97.0] \n", + "input_features = pd.DataFrame([input_features], columns=X.columns)\n", + "output = ridge_model.predict(input_features)\n", + "print(\"Output: \", output[0])" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "4ffcbb4b-d8df-48f2-bb0f-4e61aa175856", + "metadata": {}, + "outputs": [], + "source": [] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "28a1a2bf-12b1-43e0-a91c-a6a348cffdb5", + "metadata": {}, + "outputs": [], + "source": [ + "import pickle" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "f826e163-0eee-4281-96d0-9c2207b9e01f", + "metadata": {}, + "outputs": [], + "source": [ + "with open('ridge_model.pkl', 'wb') as f:\n", + " pickle.dump(ridge_model, f)\n", + "\n", + "print(\"Model saved to ridge_model.pkl\")" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "fb1bf9f9-cc41-4854-8271-710095244c6a", + "metadata": {}, + "outputs": [], + "source": [ + "with open('ridge_model.pkl', 'rb') as f:\n", + " loaded_model = pickle.load(f)\n", + "\n", + "input_features = []\n", + "for i in ['Body mass index', 'Weight', 'Disciplinary failure','Reason','Age','Service time','Work load Average/day','Hit target']:\n", + " input_features.append(input(i))\n", + "input_features = pd.DataFrame([input_features], columns=X.columns)\n", + "output = loaded_model.predict(input_features)\n", + "print(\"Output: \", output[0])" + ] + }, + { + "cell_type": "markdown", + "id": "d086c7ef-c6a9-46fc-a356-5fa6230e1fff", + "metadata": {}, + "source": [ + "# **Model is affected by Spurious Correlation**" + ] + } + ], + "metadata": { + "kernelspec": { + "display_name": "Python 3 (ipykernel)", + "language": "python", + "name": "python3" + } + }, + "nbformat": 4, + "nbformat_minor": 5 +} diff --git a/Machine_Learning/Absent_Time_prediction_using_HyperParameter_Tuning/model/ridge_model.pkl b/Machine_Learning/Absent_Time_prediction_using_HyperParameter_Tuning/model/ridge_model.pkl new file mode 100644 index 0000000000..5b59e8def9 Binary files /dev/null and b/Machine_Learning/Absent_Time_prediction_using_HyperParameter_Tuning/model/ridge_model.pkl differ diff --git a/Machine_Learning/Absent_Time_prediction_using_HyperParameter_Tuning/requirements.txt b/Machine_Learning/Absent_Time_prediction_using_HyperParameter_Tuning/requirements.txt new file mode 100644 index 0000000000..f02aa8cd23 Binary files /dev/null and b/Machine_Learning/Absent_Time_prediction_using_HyperParameter_Tuning/requirements.txt differ