Hands-on Logistic Regression Case Study | Data Science using Python

Welcome to our exciting data science case study! In this project, we'll be applying logistic regression to a real-world dataset obtained from Kaggle. Our dataset is the Smoke Detection Dataset.

📊 Dataset Overview
The Smoke Detection Dataset contains various features that help in detecting smoke. Our goal is to build a logistic regression model to accurately classify smoke detection.

🛠️ Step-by-Step Approach
Data Preprocessing 🔍

Missing Values: We'll detect and treat any missing values to ensure our dataset is clean.
Outliers: Identify and handle outliers to improve model accuracy.
Multicollinearity: Check for multicollinearity and eliminate redundant features.
Feature Elimination: Select the most important features for our model.
Model Building and Evaluation 📈

We'll apply logistic regression using two powerful libraries: sklearn and statsmodels.
Compare the performance of models built using both libraries.
Visualization 📉

Use visual tools to understand the relationships between features and the target variable.
Plot the results to interpret the model's performance.
🧰 Tools and Libraries
Python 🐍
sklearn 🤖
statsmodels 📊

🌟 Learning Outcomes
By the end of this case study, you'll have hands-on experience with:
Handling missing values and outliers
Dealing with multicollinearity
Feature elimination techniques
Building and evaluating logistic regression models using sklearn and statsmodels
Visualizing data and model results

Join us on this learning journey and enhance your data science skills! 💡

[corentinbialais]

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