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Predictive Modeling with Python

Build predictive models using Python libraries like sci-kit-learn. Learn to apply machine learning techniques to solve business problems.

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Certificate :

After Completion

Start Date :

10-Jan-2025

Duration :

30 Days

Course fee :

$150

COURSE DESCRIPTION:

  1. Harness predictive modeling capabilities with Python and its extensive data science libraries.

  2. This course covers the construction, evaluation, and deployment of predictive models across various applications.

  3. Topics include forecasting, classification, and regression analysis.

  4. Gain skills in data processing, algorithm selection, and model performance assessment.

  5. Achieve precise predictions applicable to real-world situations.

CERTIFICATION:

  1. Earn a Certified Predictive Modeling Practitioner with a Python credential to showcase your skills in developing and deploying predictive models.

LEARNING OUTCOMES:

By the conclusion of the course, participants will possess the skills to:

  1.  Grasp essential principles of predictive modeling, focusing on regression, classification, and clustering techniques.

  2. Utilize libraries like Pandas and NumPy for data preprocessing and cleaning.

  3. Develop predictive models employing well-known algorithms, including linear regression, decision trees, and random forests.

  4. Assess model effectiveness through metrics such as accuracy, precision, recall, and AUC-ROC.

  5. Optimize model performance using methods like cross-validation and hyperparameter tuning, and implement models in production for real-time predictions.

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Course Curriculum

Introduction to Predictive Modeling
  1. What is Predictive Modeling?
    • Definition and significance in data science and machine learning.
    • Overview of the predictive modeling process: data collection, data preprocessing, feature engineering, model building, evaluation, and deployment.
  2. Applications of Predictive Modeling
    • Business applications: customer segmentation, sales forecasting, and churn prediction.
    • Healthcare: disease diagnosis, predicting patient outcomes.
    • Finance: fraud detection, credit scoring, stock market predictions.
Data Collection and Preprocessing
  1. Understanding the Dataset
    • Types of data: structured, unstructured, categorical, and continuous data.
    • Data types and formats: numerical, text, time series, and image data.
  2. Data Cleaning
    • Handling missing data: mean, median, mode imputation, KNN imputation, or removing rows.
    • Dealing with duplicates and outliers.
  3. Feature Engineering
    • Feature creation: aggregating features, generating polynomial features, binning, etc.
    • Feature scaling: normalization, standardization, and transformation of features.
    • Encoding categorical variables: One-Hot Encoding, Label Encoding.
  4. Splitting the Data
    • Dividing the dataset into training, validation, and test sets (80/20 or 70/30 split).
    • Cross-validation for robust model evaluation.
Machine Learning Algorithms Overview
  1. Types of Machine Learning Models
    • Supervised learning (Regression & Classification) vs. Unsupervised learning.
    • Model categories: Linear models, tree-based models, ensemble methods, and neural networks.
  2. Model Evaluation Metrics
    • Regression metrics: Mean Absolute Error (MAE), Mean Squared Error (MSE), R-squared.
    • Classification metrics: Accuracy, Precision, Recall, F1-score, AUC-ROC curve.
Linear and Logistic Regression
  1. Linear Regression
    • Understanding simple and multiple linear regression.
    • Interpreting the regression coefficients.
    • Model fitting, predicting, and evaluating using scikit-learn.
    • Regularization: Ridge and Lasso regression to prevent overfitting.
  2. Logistic Regression
    • Concept of logistic regression for binary classification.
    • Sigmoid function and its output interpretation.
    • Implementing logistic regression using scikit-learn.
    • Model evaluation: confusion matrix, precision, recall, and ROC curves.
Decision Trees and Random Forests
  1. Decision Trees
    • Concept of decision trees for both regression and classification.
    • Gini impurity, entropy, and information gain for splitting nodes.
    • Handling overfitting using pruning techniques.
    • Implementing decision trees using scikit-learn.
  2. Random Forests
    • Introduction to ensemble methods: combining multiple decision trees.
    • Bagging (Bootstrap Aggregating) and its impact on reducing overfitting.
    • Hyperparameter tuning: n_estimators, max_depth, etc.
    • Feature importance in Random Forests and its application in predictive modeling.
Support Vector Machines (SVM)
  1. Understanding SVM
    • Concept of Support Vector Machines for classification problems.
    • Linear and non-linear decision boundaries using kernels (e.g., RBF, Polynomial).
    • Maximizing the margin and minimizing classification error.
    • Implementing SVM using scikit-learn.
    • Hyperparameter tuning: C, gamma, and kernel types.
K-Nearest Neighbors (KNN)
  1. KNN Algorithm
    • Understanding the K-Nearest Neighbors algorithm for classification and regression.
    • Distance metrics: Euclidean, Manhattan, Minkowski.
    • Choosing the right value for k.
    • Implementing KNN using scikit-learn.
    • Model evaluation using cross-validation and grid search for hyperparameter tuning.
Ensemble Learning and Boosting
  1. Ensemble Learning
    • Introduction to ensemble methods: combining the predictions of multiple models.
    • Bagging, Boosting, and Stacking.
  2. Boosting Algorithms
    • AdaBoost: Adaptive boosting to improve weak learners.
    • Gradient Boosting: How it builds models iteratively to correct errors.
    • XGBoost: Efficient implementation of gradient boosting.
    • LightGBM: A faster gradient boosting framework for large datasets.
    • Hyperparameter tuning and cross-validation.
Model Evaluation and Hyperparameter Tuning
  1. Cross-Validation
    • K-fold cross-validation and its importance in model evaluation.
    • Stratified k-fold cross-validation for imbalanced datasets.
  2. Hyperparameter Tuning
    • Grid search vs. random search for hyperparameter optimization.
    • Implementing grid search using GridSearchCV in scikit-learn.
    • Using random search for large parameter spaces.
  3. Model Validation and Testing
    • Evaluating model performance on test data.
    • Analyzing confusion matrix, AUC-ROC, and precision-recall curves for classification problems.
    • Error analysis for regression models.
Capstone Project
  1. Predictive Modeling End-to-End Project
    • Implement a complete predictive modeling project, from data collection to model deployment.
    • Focus on a real-world problem: customer churn prediction, demand forecasting, or risk analysis.
    • The project will include data preprocessing, model building, evaluation, and deployment using Python and machine learning frameworks.

Training Features

Hands-on Learning

Apply predictive modeling techniques to real-world datasets using scikit-learn, pandas, and matplotlib.

End-to-End Project

Build an end-to-end predictive model: from data preparation to deployment.

Industry-Relevant Tools

Use popular Python libraries for data analysis and machine learning: pandas, scikit-learn, matplotlib, seaborn.

Practical Applications

Gain experience with models used in various industries, such as finance, healthcare, and e-commerce.

Real-Time Feedback

Get personalized feedback on assignments and projects to enhance learning.

Certification

Receive a certificate upon completion, demonstrating your expertise in predictive modeling with Python.

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