This repository contains some Python projects.
The project's goal was to apply Python and pandas to analyze marketing data and translate data science technical skills into practical insights. It aimed to answer key business questions, such as campaign performance and channel effectiveness, using a simulated online subscription business dataset.
- Python Programming
- Pandas: Importing datasets, data manipulation (merging, slicing, grouping), updating data types, adding new columns
- Matplotlib: Data visualization
- Statistical Analysis: Calculating conversion and retention rates, A/B testing
- Data Cleaning: Resolving inconsistencies and preparing data for analysis
- Data Importing and Cleaning: Imported datasets and performed initial exploratory analysis. Cleaned and preprocessed data to ensure accuracy for analysis.
- Exploratory Analysis and Metrics Calculation: Analyzed common marketing metrics like conversion and retention rates. Conducted user segmentation and compared performance across different segments.
- Conversion Attribution: Built functions to automate analysis. Identified inconsistencies in conversion rates and resolved data errors.
- A/B Testing: Analyzed A/B test results, focusing on segmentation. Evaluated statistical significance and calculated lift.
- Visualization: Created visualizations to communicate key insights. Plotted campaign results and daily marketing reach.
- Campaign Analysis: Identified the most effective marketing campaigns and channels, highlighting underperforming areas.
- User Segmentation: Provided insights into customer segments, improving targeting strategies.
- A/B Testing: Demonstrated the importance of segmentation and evaluated the impact of marketing strategies.
- Actionable Insights: Delivered data-driven recommendations for marketing optimization, reinforcing the value of data analysis in strategic decision-making.
It provided actionable insights on campaign performance and channel effectiveness, aiding strategic decision-making and optimization of marketing efforts.
Leverage AI and data analysis to classify Nasdaq-100 stocks into sectors and summarize year-to-date (YTD) performance, highlighting top-performing sectors and companies.
- Python & Pandas: Data manipulation and analysis.
- OpenAI API: Automated sector classification and performance summarization.
- Data Analysis: Calculation and interpretation of YTD performance metrics.
- Data Integration: Imported and processed Nasdaq-100 stock data using pandas.
- AI Classification: Used OpenAI API to classify stocks into sectors like Technology, Healthcare, and Financial.
- Performance Summary: Generated a comprehensive YTD performance summary, identifying top-performing sectors and companies.
- Stock Recommendations: Provided actionable recommendations for investors based on performance data.
- Enriched Dataset: Successfully classified stocks into relevant sectors, enhancing data quality.
- Performance Insights: Identified the top three performing sectors and highlighted top companies within each.
- Informed Decision-Making: Delivered strategic insights, enabling data-driven investment decisions.
- Investment Strategy: Facilitated informed investment strategies by highlighting top-performing sectors and stocks.
- Efficiency Gains: Automated classification and summarization processes, enhancing analysis efficiency.
- Market Analysis: Offered a comprehensive overview of sector performance, aiding in market trend analysis and strategic planning.
The goal of this project was to forecast retail sales using advanced time series analysis techniques. By leveraging historical sales data from a Superstore dataset, the project aimed to predict future sales trends, identify seasonal patterns, and provide actionable insights to optimize inventory management and improve strategic decision-making.
- Data Cleaning and Preparation: Processed raw sales data by removing unnecessary columns, handling missing values, and aggregating sales by date.
- Exploratory Data Analysis (EDA): Visualized data to uncover trends, seasonality, and patterns using Python libraries such as pandas, matplotlib, and seaborn.
- Time Series Decomposition: Decomposed the sales data into trend, seasonality, and noise components to understand underlying patterns.
- Modeling and Forecasting: Utilized ARIMA (AutoRegressive Integrated Moving Average) and Prophet models to build robust forecasts. Conducted parameter tuning and validation to optimize model performance.
- Validation and Metrics: Evaluated model accuracy using metrics like Mean Squared Error (MSE) and Root Mean Squared Error (RMSE).
- Visualization: Created detailed visualizations to compare observed and forecasted sales, highlighting key trends and future predictions.
- Accurate Forecasts: The ARIMA and Prophet models provided accurate sales forecasts, with the ARIMA model achieving a low MSE of 22993.58 and an RMSE of 151.64.
- Seasonal Patterns: Identified clear seasonal trends, with sales peaking towards the end of the year and dipping at the beginning.
- Category Comparison: Analyzed and compared sales trends between Furniture and Office Supplies, revealing that Office Supplies had a stronger growth trend over time.
- Future Sales Predictions: Produced forecasts for the next 36 months, providing valuable insights into expected sales trends and potential peak periods.
- Inventory Management: By accurately forecasting sales, businesses can optimize their inventory levels, reducing costs associated with overstocking or stockouts.
- Strategic Planning: Understanding future sales trends enables better strategic planning and resource allocation, ensuring that businesses are prepared for peak periods.
- Marketing Campaigns: Insights into seasonal patterns allow businesses to time their marketing campaigns effectively, maximizing their impact during high-sales periods.
- Competitive Advantage: Advanced forecasting provides a competitive edge, allowing businesses to anticipate market trends and make data-driven decisions.
- Taken the movie dataset from Kaggle - https://www.kaggle.com/danielgrijalvas/movies
- Used some libraries such as Panda, Numpy, Seaborn, Matplotlib to perform some operations.
- Plotted correlation between all the features of the dataset.
- Used Jupyter Notebook to write the code.