Proficient in data analysis and handling large data sets and using them to solve business problems. Skilled at Data Mining and analyzing large volumes of data. A keen eye for detail to observe data trends across short an…
Proficient in data analysis and handling large data sets and using them to solve business problems. Skilled at Data Mining and analyzing large volumes of data. A keen eye for detail to observe data trends across short and long-term periods. Great at navigating complex quantitative data.
Predicted the sale price for each house.
Minimized the difference between predicted and actual rating (MSE).
Performed exploratory data analysis.
Checked null value percentage for each feature.
Visualized missing null values using heat map.
Imputed missing values.
Used pandas, numpy, matplotlib, seaborn, and scikit learn libraries for exploratory data analysis.
Created new variables and transformed existing ones for feature engineering.
Applied one-hot encoding for categorical variables.
Handled missing data and outliers using Pandas and Scikit-learn's preprocessing module.
Developed a regression model using Linear Regression, Random Forest, and Gradient Boosting to predict hourly bike demand.
Designed the data pipeline.
Understood the impact of features such as time of day, weather conditions, and holidays.
Engineered features.
Experimented with hyperparameter tuning techniques such as Random Search and Grid Search.
Achieved an R2 score of 90% using Gradient Boosting algorithm.
Developed a Regression model to predict the count of rental bikes required on an hourly basis.
Applied ML algorithms such as Linear Regression, Decision Tree Regressor, Random Forest Regressor, Gradient Boosting Regressor, and regularization techniques like ridge and lasso.
Created data transformations and manipulations to link sales, orders, vendors, and customers to create a data model.
Created KPI matrices based on net value, total sales, and region-wise profit and loss parameters in Power BI.
Explored and analyzed the data to discover key factors responsible for customer churn.
Developed recommendations to ensure customer retention.
Used Tableau, Matplotlib, Seaborn, and Plotly for visualization, storytelling, and experimenting with charts.
Used Python, Pandas, NumPy, Scikit-learn, and XGBoost for telecom churn EDA.
Used big data technologies such as Hadoop and Apache to handle large datasets.
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