Aspiring Data Analyst with knowledge of Excel, SQL, Python, Power BI, Tableau, and data visualization techniques. Skilled in data cleaning, exploratory data analysis (EDA), dashboard creation, and generating business ins…
Aspiring Data Analyst with knowledge of Excel, SQL, Python, Power BI, Tableau, and data visualization techniques. Skilled in data cleaning, exploratory data analysis (EDA), dashboard creation, and generating business insights from raw datasets. Experienced in working on analytics projects such as IPL Data Analytics Report using Python libraries like Pandas, NumPy, Matplotlib, and Seaborn. Passionate about transforming data into meaningful insights and continuously learning AI, machine learning, and business intelligence tools to solve real-world problems.
Excited to share my latest project: Customer Churn PredictionI built a Customer Churn Analysis project using Python and Pandas, aimed at helping businesses identify customers who are most likely to leave their services. This project gave me hands-on experience in data analysis, machine learning, and interactive web applications. 📈 👋 Here’s what I did in the project:🔹 Data Cleaning & Preprocessing: Used Pandas to clean the dataset, handle missing values, and convert data into a format suitable for modeling.🔹 Exploratory Data Analysis (EDA): Analyzed customer demographics, service usage patterns, and behavior trends to understand what factors contribute to churn. Visualized the insights using plots and charts to uncover meaningful patterns.🔹 Feature Engineering: Identified and transformed key features that impact churn, ensuring the model receives accurate and relevant information.🔹 Machine Learning Model: Developed a Logistic Regression model to predict the probability of customer churn. The model was trained to identify high-risk customers with precision.🔹 Interactive Web Application: Created a Streamlit app for both single and batch predictions. Users can input customer details or upload CSV files to get churn predictions along with probability scores.🔹 Feature Alignment & Robustness: Ensured that the app handles input data properly, aligns feature columns with the model, and provides accurate predictions without errors.
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