https://github.com/madhurragarwal/advertising-data-set---eda-and-ml
Logistic Regression and EDA done on Advertising Data set
https://github.com/madhurragarwal/advertising-data-set---eda-and-ml
data-analysis machine-learning
Last synced: 3 months ago
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Logistic Regression and EDA done on Advertising Data set
- Host: GitHub
- URL: https://github.com/madhurragarwal/advertising-data-set---eda-and-ml
- Owner: madhurragarwal
- Created: 2023-09-17T16:40:35.000Z (almost 3 years ago)
- Default Branch: main
- Last Pushed: 2023-09-17T17:06:09.000Z (almost 3 years ago)
- Last Synced: 2025-11-04T06:19:16.598Z (9 months ago)
- Topics: data-analysis, machine-learning
- Language: Jupyter Notebook
- Homepage:
- Size: 959 KB
- Stars: 0
- Watchers: 1
- Forks: 0
- Open Issues: 0
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Metadata Files:
- Readme: README.md
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README
## Advertising Dataset Analysis and Logistic Regression Model
# Table of Contents
1. Introduction
2. Dataset Overview
1. Exploratory Data Analysis (EDA)
2. Data Preprocessing
3. Data Exploration
3. Logistic Regression Model
1. Data Splitting
2. Model Training
3. Model Evaluation
4. Conclusion
# 1. Introduction
This README document provides an overview of an advertising dataset and outlines the steps taken for exploratory data analysis (EDA) and the development of a logistic regression model to predict the likelihood of a customer clicking on an online advertisement.
# 2. Dataset Overview
1. Dataset Name: advertising.csv
2. Data Source: Kaggle
3. Data Description: The dataset contains information related to online advertising campaigns. It includes various features such as age, gender, daily time spent on the internet, etc., and a binary target variable indicating whether or not a customer clicked on the advertisement.
# 3. Exploratory Data Analysis (EDA)
# Data Preprocessing
1. Missing Data: Check for missing values and handle them if necessary.
2. Data Cleaning: Remove any outliers or irrelevant data points.
3. Feature Engineering: Create new features if needed.
# Data Exploration
1. Univariate Analysis: Explore individual features using histograms, box plots, etc.
2. Bivariate Analysis: Investigate relationships between features and the target variable.
3. Multivariate Analysis: Examine correlations between features and identify any patterns.
# 4. Logistic Regression
# Data Splitting
Split the dataset into training and testing sets.
# Model Training
1. Use logistic regression to build a predictive model.
2. Train the model using the training dataset.
3. Perform hyperparameter tuning if necessary.
# Model Evaluation
1. Evaluate the model's performance using appropriate metrics such as accuracy, precision, recall, F1-score, and ROC AUC.
2. Visualize the results using confusion matrices or ROC curves.
3. Interpret the coefficients of the logistic regression model.
# 5. Conclusion
In conclusion, this project involves the analysis of an advertising dataset to gain insights into customer behavior and the development of a logistic regression model to predict advertisement click-through rates. By following the steps outlined in this README, you will be able to perform EDA, preprocess the data, train a logistic regression model, and evaluate its performance.
Feel free to customize this README document to include specific details about your dataset, analysis, and model. Additionally, provide code and visualizations in separate Jupyter Notebook or Python script files to accompany this documentation.