Analyzed Airbnb dataset to understand correlations between features affecting pricing.
Provided by YBI Foundation
- Data exploration & cleaning
- Correlation analysis & visualization
- Summarized insights
- Python pandas, data visualization, analytical thinking
Performed EDA on credit card transactions to understand patterns in fraudulent activity.
Provided by YBI Foundation
- Explored dataset, cleaned data
- Distribution analysis & correlation matrix
- Documented observations
- Python, pandas, data visualization
Analyzed sales data to find patterns, trends, and insights on product performance.
Dataset: Provided by YBI Foundation during training
Help understand which products are performing well and identify sales trends.
- Data cleaning and preprocessing
- Exploratory data analysis (sales over time, product categories)
- Visualizations to present insights
- Key findings documented
- Learned to interpret business data
- Strengthened data cleaning and plotting skills
- Learned to summarize insights effectively
- Predictive sales forecasting
- Advanced analytics with machine learning
Built analysis to predict NBA player market prices based on player statistics and performance data.
Dataset: Provided by YBI Foundation during training
Understand key factors influencing player prices and predict them using statistical techniques.
- Data cleaning and handling missing values
- Feature selection and correlation analysis
- Basic predictive modeling
- Visualization of predictions vs actual
- Practiced regression and feature importance
- Improved Python data handling skills
- Learned to present predictions clearly
- Implement advanced ML models for better accuracy
- Integrate real-time performance data
Analyzed financial news data to find patterns, trends, and sentiment affecting markets.
Dataset: Provided by YBI Foundation during training
Extract insights and trends from financial news articles.
- Data cleaning and preprocessing text data
- Basic sentiment analysis
- Visualizations of trends
- Summarized insights
- Learned text preprocessing and basic NLP
- Improved ability to extract actionable insights from text
- Built visualization skills for textual data
- Advanced NLP for sentiment classification
- Integration with stock market prediction
Analyzed medical insurance dataset to predict insurance costs based on personal and health features.
Dataset: Provided by YBI Foundation during training
Understand key factors influencing insurance costs and build predictive analysis.
- Data cleaning and handling missing values
- Exploratory analysis on factors affecting cost
- Simple predictive modeling (optional)
- Visualization of findings
- Practiced regression analysis
- Learned to clean and explore medical datasets
- Improved ability to extract actionable insights
- Use ML models for better prediction
- Deploy a small interactive cost prediction tool