PRAKHAR SAXENA

Positive problem-solver with knack for uncovering insights through data-driven analysis. Well-versed in statistical methodologies and proficient in tools such as SQL and Python, with solid understanding of data visualization techniques. Capable of leveraging analytical skills to drive impactful business decisions.

• Successfully completed a Data Analyst course.
• Successfully completed a Python course.
• Successfully completed a SQL course.
• Successfully completed an AWS course.

🎬 Movie Recommender System
Technologies: Python, Pandas, NumPy, Scikit-Learn, Surprise, TensorFlow, Flask, Streamlit, AWS

• Developed a movie recommendation system using content-based and collaborative filtering.
• Implemented ML models like SVD, ALS, and Cosine Similarity for accurate predictions.
• Processed MovieLens & IMDb datasets and deployed the app using Flask/Streamlit on AWS.
• Evaluated model performance using RMSE, Precision, and Recall.
• Integrated user feedback to improve recommendation quality over time.

📊 Netflix Data Analysis
Technologies: Python, Pandas, NumPy, Matplotlib, Seaborn, Power BI

• Performed EDA to analyze genre trends, release patterns, and content ratings.
• Cleaned and transformed Netflix dataset using Pandas and NumPy.
• Visualized insights using Matplotlib, Seaborn, and interactive Power BI dashboards.
• Delivered data-driven insights to support content strategy decisions.

✈️ Airport Data Analysis (SQL Project)
Technologies: MySQL, SQL Server, Power BI, MS Excel

• Queried flight, booking, and airline data to uncover traffic and delay patterns.
• Used joins, subqueries, and aggregations to analyze performance across routes and airports.
• Created views and stored procedures for reusable analytics tasks.
• Built dashboards in Power BI to visualize delays, traffic volume, and performance trends.

📈 PROJECT 1 – Sales Data Analysis
-Analyzed sales trends, revenue, and customer behavior using Power BI dashboards to support business growth decisions.

🛒 PROJECT 2 – Blinkit Data Analysis
-Visualized operational and order data from Blinkit to identify performance bottlenecks and optimize delivery efficiency.

💳 PROJECT 3 – UPI Transactions Data Analysis
-Explored UPI transaction trends, volume, and user patterns to uncover digital payment growth insights using Power BI.