The main objective of this project is to provide learners with hands-on experience in data analysis research. By the end of the internship, learners are expected to:

1. Collaborate with data scientists and analysts: Interns will work closely with experienced professionals to collect, clean, and analyze data. They’ll gain insights into real-world data science workflows.
2. Develop data pipelines and automate tasks: Interns will learn how to build efficient data pipelines, ensuring smooth data flow from various sources. Automation skills are crucial for scalability.
3. Conduct exploratory data analysis (EDA): Interns will explore datasets, visualize patterns, and identify trends. EDA helps uncover valuable insights and informs subsequent analyses.
4. Support research initiatives: Interns will contribute to ongoing projects related to buyer behavior, pricing, and inventory management. They’ll apply statistical techniques and propose actionable recommendations.
5. Present findings: Effective communication is key. Interns will learn to present their insights clearly to the team, bridging the gap between data and decision-making.

• Overall, this project aims to equip learners with practical skills and prepare them for data-driven roles.

• Data Collection and Cleaning:Gather relevant data from various sources (e.g., databases, APIs, CSV files).
• Clean and preprocess the data to ensure its quality and consistency.
• Exploratory Data Analysis (EDA):Conduct EDA to understand the data distribution, identify outliers, and visualize patterns.
• Use statistical techniques to summarize key insights.
• Feature Engineering:Create new features or transform existing ones to enhance predictive power.
• Consider domain-specific knowledge to engineer meaningful features.
• Model Building:Develop predictive models (e.g., regression, classification) based on the problem statement.
• Evaluate model performance using appropriate metrics (e.g., accuracy, RMSE).
• Hyperparameter Tuning:Optimize model hyperparameters to improve performance.
• Use techniques like grid search or random search.
• Interpretability and Insights:Interpret model results and understand feature importance.
• Provide actionable insights to stakeholders.
• Documentation and Reporting:Document the entire process, including data sources, preprocessing steps, and modeling details.
• Prepare a clear and concise report summarizing findings and recommendations.