Tejas Tatte

: 🚀 Visualizing Data in 3D: A Step Toward Better Insights! 📊🔍 In my recent data science project, I explored 3D plotting to better understand complex relationships between variables. Using Python (Matplotlib & Plotly), I created a 3D scatter plot to visualize multi-dimensional data trends. 📌 Why 3D Plots? ✅ Identify hidden patterns in high-dimensional datasets ✅ Improve feature selection for machine learning models ✅ Enhance data storytelling & decision-making Here’s a quick look at the plot I created! #DataScience #MachineLearning #Python #3DVisualization #Matplotlib #Plotly #AI

🚀 Deep Learning Project using TensorFlow 🎯 I recently worked on an exciting deep learning project leveraging TensorFlow to solve a complex problem in [mention domain, e.g., image classification, NLP, anomaly detection, etc.]. This project involved: 🔹 Building & Training deep neural networks for high accuracy 🔹 Data Preprocessing & Augmentation to enhance model performance 🔹 Optimization & Fine-Tuning using techniques like batch normalization, dropout, and hyperparameter tuning 🔹 Model Deployment using TensorFlow Serving / Flask for real-world application 📌 Tech Stack: TensorFlow, Keras, Python, NumPy, Pandas, Matplotlib, OpenCV (if applicable) This project not only strengthened my deep learning expertise but also helped me understand real-world challenges in AI model deployment. 💡 Always open to discussions and collaborations on AI & ML! Let’s connect. #DeepLearning #TensorFlow #MachineLearning #AI #Python

🚀 Red Wine Quality Prediction using Python & Machine Learning 🍷 Developed a machine learning model to predict the quality of red wine based on its chemical composition. This project focuses on data preprocessing, model selection, and performance optimization using Python. 🔹 Project Highlights: ✅ Data Preprocessing – Handled missing values, feature scaling, and outlier detection. ✅ Exploratory Data Analysis (EDA) – Visualized correlations between acidity, sugar, pH, and quality. ✅ Machine Learning Models – Implemented Logistic Regression, Random Forest, Decision Tree, and XGBoost for classification. ✅ Model Evaluation – Compared models using accuracy, precision, recall, F1-score, and ROC-AUC. ✅ Deployment – Integrated the model into a Flask API for real-time wine quality predictions. 🛠 Tech Stack: 🔹 Python | Pandas | NumPy | Scikit-Learn | Matplotlib | Seaborn | Flask 💡 This project enhanced my expertise in Python for data science, feature engineering, and model optimization. Excited to work on more ML-driven projects! 🚀 #Python #MachineLearning #DataScience #AI #WineQualityPrediction #Flask

Designed end-to-end analytics on 10,000+ patient records using Python, SQL, Power BI, Tableau, and Excel. Built multi-page KPI dashboards tracking ₹52.47L revenue, Follow-Up Rate, and Monthly Visit Trends. Identified the top five diagnosed conditions and doctor workload imbalances to support capacity planning and resource allocation.

Built an analytics pipeline for 12,450+ vehicle sales records using Python, SQL, Power BI, Tableau, and Excel. Extracted KPIs including ₹84.2 Cr revenue and 3.2-day service turnaround. Identified compact SUVs as the top growth segment and recommended changes projected to reduce turnaround from 3.2 to 2.0 days.