Bharat J

Computer Science graduate with expertise in statistical analysis and data visualization, delivering actionable insights for business growth. Proficient in Python, SQL, and Power BI for collecting, cleaning, and interpreting large datasets. Experienced in designing dashboards, applying segmentation models, and performing feature selection to enhance decision-making processes. Strong background in data analysis, quality assurance, and testing. Focused on optimizing organizational processes and identifying trends.

• Technologies used: Python, Power BI
• Summary: Conducted a customer segmentation analysis using the RFM (Recency, Frequency, Monetary) model, segmenting customers into 11 distinct groups based on purchasing behaviors. Enhanced marketing efficiency and increased sales revenue through Power BI-driven insight visualization.
• Responsibilities: Data collection, quality assessment using Python, RFM calculation, actionable insight extraction, and developing interactive dashboards with Power BI to support targeted marketing strategies.

• Technologies used: Python, Pandas, NumPy, Seaborn, Jupyter Notebook
• Summary: Applied Recursive Feature Clustering (RFC) on a fast-food dataset to identify key nutritional features, improving model interpretability and efficiency.
• Responsibilities: Data cleaning (handling missing values, outliers, duplicates), exploratory analysis, hierarchical clustering, and feature selection of calcium, calories, cholesterol, vitamin C, and vitamin A.

• Technologies used: Python, TensorFlow, OpenCV, Google Colab, CNN, MobileNetV2
• Summary: Built a deep learning system using CNN and MobileNetV2 for automated classification of rice leaf diseases and pests via image data. Published work based on this project.
• Responsibilities: Image preprocessing, feature extraction, data augmentation to improve model accuracy, and interface development for farmer-friendly image uploads and predictions.

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