This repository contains a collection of data analysis and machine learning projects developed during my internship. Each project leverages different datasets and applies various techniques to provide insights and predictions.
This project analyzes advertising data to uncover insights about the effectiveness of different advertising strategies. It explores various metrics to understand how advertisements impact consumer behavior.
This project uses machine learning techniques to predict car prices based on multiple features such as brand, model, year, mileage, and more. It helps users understand the factors influencing car prices and provides accurate pricing predictions.
This project focuses on classifying different species of iris flowers using classification algorithms. It employs the famous Iris dataset to demonstrate how supervised learning techniques can be applied to solve classification problems.
This project analyzes unemployment data to visualize trends and understand the economic factors affecting unemployment rates. It utilizes data visualization techniques to present findings and highlight key trends in the labor market.
- Pandas: For data manipulation and analysis
- Matplotlib: For creating static, interactive, and animated visualizations
- Seaborn: For statistical data visualization
- Scikit-Learn: For machine learning algorithms and data preprocessing
- Data Exploration: Initial data inspection and cleaning
- Exploratory Data Analysis (EDA): Visualization and interpretation of data patterns
- Basic Analysis: Descriptive statistics and insights
- Machine Learning: Implementation of predictive models and algorithms to enhance analysis and provide data-driven insights
- Data Loading: Importing and preparing data for analysis
- Visualization: Creating plots to understand data distributions and trends
- Statistical Analysis: Computing summary statistics and correlations
- Machine Learning: Developing and applying predictive models to derive insights and make forecasts
[Yogesh S]