classification problem

Project Overview:

Developed a logistic regression model to classify data into binary categories, demonstrating how statistical methods can be applied to real-world decision-making problems such as customer churn prediction, loan approval, or disease diagnosis.

Key Steps:

Data Preprocessing – Cleaned missing values, encoded categorical variables, and standardized features for better model performance.

Exploratory Data Analysis (EDA) – Visualized class distributions and identified correlations between predictors and target variable.

Modeling – Implemented Logistic Regression to estimate class probabilities and decision boundaries.

Evaluation – Assessed the model using accuracy, precision, recall, F1-score, and ROC-AUC.

Results – Logistic Regression provided interpretable coefficients, showing which features strongly influenced the classification outcome.

Tech Stack:

Python, Scikit-learn, Pandas, NumPy, Matplotlib, Seaborn

Impact:

This project highlights how logistic regression can deliver both accurate predictions and interpretable insights, making it highly valuable for business and research applications where transparency matters.