Chandan Reddy N V

• A Business Data analyst having practical experience with SQL, Python statistical programming, Tableau and Microsoft Excel.
• Possess a strong critical thinking skill, able to analyze data, identify patterns and extract actionable insights and information based on given data.
• An excellent communicator able to translate technical information to decision-makers.

Microsoft Excel and Tableau Project on FMCG Retails. 

• Worked on analyzing the retail data and generated insights from the data.
• Prepared and cleaned data using Excel functions like Index-Match, year, month, if, and, or.
• Created pivot tables, connected all pivot tables using slicer, and created required charts for developing a dashboard.
• Also designed and developed a Tableau dashboard for comprehensive visualization using groups, join, calculated field and filters.

SQL Project on Customer Order Pattern Analysis

• Firstly, tables were created to store user profiles, preferences, and order histories efficiently.
• Performed data cleaning tasks such as removing duplicate records from the orders data.
• Developed query logic to extract relevant information from the tables. Summarized data using group by, joins, CTE, window function, and nested queries to gain insights into customer behavior.
• Additionally, calculations for cumulative sums and totals were created to further analyze the order patterns effectively.

Machine Learning Projects

Automobile Mileage Prediction using Linear Regression

• Performed data cleaning and preparation, ensuring the dataset was suitable for analysis.
• Performed Exploratory Data Analysis (EDA) to gain insights into the data and identify potential patterns or trends.
• Applied Feature engineering techniques, including outlier treatment, imputation, and encoding, to enhance the quality and relevance of the features.
• Built a Linear Regression model using the training sample, leveraging the prepared data and engineered features.
• Tested the model, yielding an accuracy of 0.84, indicating its effectiveness in predicting automobile mileage.

Bank investment prediction using logistic regression

• Utilized a sample dataset comprising over 41,000 records.
• Employed Feature engineering, Exploratory Data Analysis (EDA), and feature selection techniques to identify the key drivers influencing bank investment decisions.
• Built a logistic regression model. This model was rigorously tested and achieved an impressive accuracy of 92% and an F1 score of 0.6, indicating its robust performance.
• Additionally, applied other algorithms including k-NN, Naive bayes, decision tree.