Linkdin Github I’m a data analyst with a passion for transforming data into valuable insights. Welcome to my GitHub profile, where you’ll find a collection of my data-driven projects and explorations.

🌼 About Me 🌼

• 🎓 B.Tech in Computer Science BIGCE, Solapur, India ( 9.29 CGPA )
• 💡 Strong believer in the power of data to drive decisions.

🌼 Work Experience 🌼

Data Analyst | TNT | JAN 2023 - FEB 2024

🌼 Tableau Projects 🌼

1) Covid-19

• Comprehensive Analysis: The project aimed to conduct a thorough analysis of COVID-19 data to identify trends and insights, informing public health policies and interventions.

• Data Utilization: Utilized WHO’s publicly available COVID-19 datasets, including daily updates on cases, deaths, recoveries, and testing rates, along with demographic and geographical information.

• Technical Approach: Employed data cleaning, preprocessing, statistical analysis, and visualization using Python libraries (Pandas, NumPy, Matplotlib, Seaborn) to analyze the pandemic’s impact and predict future trends.

2) IPL Data Analysis

• Comprehensive Analysis of IPL Data: Aimed to derive actionable insights and trends from extensive IPL data, focusing on performance metrics, player statistics, team dynamics, and match outcomes to pinpoint success factors.

• Advanced Data Techniques: Engaged in exploratory data analysis (EDA), sentiment analysis, and predictive modeling to interpret the complex data landscape of the IPL.

• Utilization of Diverse Skillset: Applied a range of skills including data cleaning, preprocessing, statistical analysis, data visualization, and machine learning, leveraging Python and its libraries like Pandas, NumPy, Matplotlib, and Seaborn for effective data manipulation and insights communication.

3) Agriculture product prediction

• Objective: Develop a predictive model utilizing regression analysis to forecast the production of rice, wheat, and vegetables across various countries.

• Data Utilization: Employ Kaggle’s world_foods Dataset for model training, emphasizing data cleaning and statistical analysis to ensure accuracy.

• Skill Application: Apply Python programming, data visualization, trend analysis, and effective communication to interpret and present the model’s predictions.

4) Loan Approval Prediction -

• Data Collection and Preparation: Assemble loan application data, conduct exploratory analysis, and perform preprocessing and transformation to prepare the dataset for modeling.

• Feature Selection and Model Training: Identify key features that influence loan approval decisions and train a predictive model using these selected features.

• Prediction and Application: Utilize the trained model to predict the outcome of loan requests, determining whether they will be approved or denied based on applicant details.

5) Energy Consumption analysis

• Energy Analysis and Optimization: Conducted an in-depth analysis of total energy consumption and costs to identify leading providers and peak usage patterns, aiming to optimize energy use and reduce expenses in urban environments.

• Comprehensive Data Study: Undertook a thorough study of energy data across several years and cities, employing a dataset that includes monthly energy usage, costs, provider rankings, and consumption categories.

• Statistical and Visual Interpretation: Applied advanced statistical analysis and data visualization techniques to decode complex energy consumption patterns, enhancing understanding and decision-making.

6) SampleSuperstore

• Sales Trend Analysis: Utilized the Sample Superstore dataset to conduct a comprehensive analysis, identifying key sales trends to inform and enhance business strategies.

• Addressing Sales Decline: Tackled the issue of decreasing sales in specific categories and regions through targeted data exploration and visualization.

• Data Interpretation Skills: Applied advanced data analysis and visualization techniques using Excel and Tableau to extract actionable insights from the dataset.

7) Car details

• Market Analysis and Price Prediction: The project aims to analyze car market trends to predict used car prices.

• Data-Driven Insights: Utilized the CarDekho dataset to address the challenge of predicting used car prices based on features like fuel type, car name, owner history, and more.

• Technical Proficiency: Employed data cleaning, preprocessing, exploratory data analysis, and machine learning to build models for accurate price prediction.

  🌼 SQL Case Studies 🌼

1) DATA MART ANALYSIS

PDF LINK

2) DATA BANK ANALYSIS

PDF LINK

3) TEXTURE TALES ANALYSIS

PDF LINK