Data Science Predictive Modelling Course in Mohali: Online & Offline Batches

The Data Science Predictive Modelling course in Mohali at techcadd runs in both online and offline batches. Predictive modelling uses historical data to forecast outcomes such as customer churn, loan default or house prices, and the course teaches you to build, evaluate and explain these models in Python.

Batch Options: Classroom or Live Online

Predictive modelling involves a lot of experimentation, so learners benefit from quick feedback on their notebooks. Both batch formats provide that, just in different ways.

Offline Batches in Mohali

Work through modelling exercises in the classroom with a trainer available to look at your code, discuss results and suggest next steps in person.

Online Batches

Attend live sessions remotely, share your screen for doubt-solving and submit notebooks for review. This suits working professionals and learners outside the Tricity area.

Syllabus

Problem Framing

Turning a business question into a prediction target, choosing between regression and classification, and avoiding data leakage.

Regression Models

Linear regression, polynomial features, Ridge and Lasso regularisation, and metrics such as MAE, RMSE and R-squared.

Classification Models

Logistic regression, k-nearest neighbours, decision trees and support vector machines, with confusion matrices, precision, recall, F1 and ROC-AUC.

Ensemble Methods

Random forests, gradient boosting, XGBoost and LightGBM, and why ensembles often perform well on tabular data.

Model Validation and Tuning

Train-test splits, k-fold cross-validation, GridSearchCV and RandomizedSearchCV, and handling class imbalance.

Interpretation and Deployment Basics

Feature importance, SHAP values for explanation, saving models with joblib and serving predictions through a simple Flask or Streamlit app.

Projects

• Predict customer churn for a subscription business and explain the main drivers

• Estimate house prices with regression and compare regularised models

• Build a credit risk classifier and choose a decision threshold based on business cost

• Create a Streamlit app that serves predictions from a trained model

Tools and Libraries

• Python, Pandas and NumPy

• scikit-learn

• XGBoost and LightGBM

• Matplotlib and Seaborn

• SHAP

• Jupyter Notebook and Google Colab

• Streamlit or Flask

Who Can Join

• Learners who have completed Python and Pandas basics

• Data analysts who want to move from reporting to prediction

• Engineering and statistics graduates

• Working professionals preferring online or weekend batches

Basic statistics such as mean, variance and correlation will help you follow the modelling concepts.

Timings, Duration and Fees

Online and offline batches may run at different times and with different durations. Contact the techcadd Mohali team for the current fee structure, start dates and seat availability.

Career Relevance

Predictive modelling is central to data scientist, machine learning engineer and advanced analyst roles. Outcomes depend on effort, the quality of your projects and market conditions.

Frequently Asked Questions

Is the content the same in online and offline batches?

Yes, the syllabus and projects are the same; only the delivery mode differs.

Do I need to know machine learning before joining?

No, but Python and Pandas basics are needed. Machine learning concepts are taught from the beginning of the modelling process.

Which algorithms are covered?

Linear and logistic regression, decision trees, k-NN, SVM, random forests and gradient boosting methods such as XGBoost and LightGBM.

Will I learn how to explain models?

Yes, through feature importance and SHAP values, which are useful when presenting results.

Can I attend online from another city?

Yes, online batches are designed for remote learners. Confirm timings with the team.

Is a certificate provided?

Certificate details depend on completion requirements. Please confirm with the team.

Enquire About Predictive Modelling Batches at techcadd

Want to choose between online and offline batches? Contact techcadd Mohali to ask about current timings.