Ullahas Gurav

Data Analysis with over 2.3+ year of experience in the field. Proficient in utilizing Python for data analysis and well-versed in machine learning algorithms. Skilled in extracting data from SAP.

I'm specialize in transforming raw data into meaningful insights. Adept at creating interactive dashboards using data visualization tools like Power-BI, Tableau, and QlikView. Proven ability to translate complex data sets into actionable recommendations for informed business decisions. Passionate about leveraging analytical skills to contribute to data-driven strategies and foster organizational success.

Exploratory Data analysis Loan Prediction

· Identify skew, outliers and relationship among other variables in the data by using Univariate and Bivariate analysis of Loan Prediction.

Regression Housing Sale price

· Data preprocessing, EDA, for feature selection apply backward selection, MLR, Ridge, Lasso, select best Algorithm, predict sale price.

Classification Car Type

· Data preprocessing, measure performance of classification algorithms, select one of the best for prediction. Supervised Loan Prediction· Missing data treatment, Data preprocessing, apply classification algorithms to predict Loan Prediction, apply regression algorithms to predict Loan Amount and Term.

Unsupervised Similar Car recommendation

· Read data, data preprocessing, apply Kmeans Clustering, get the input from user then print the similar car by the cluster value.

Startups Analysis

· Explore and analysis the data to identify which gives best variable to predict the profit. Use Simple Linear Regression to find profit along with RND column. After it for multi-linear regression (backward elimination & forward selection) used to identify which is further good predictors to predict the profit. Formed an OLS model to identify and eliminate unnecessary columns from analysis.

Heart Disease prediction

· to understand how people has medical illness history. Performed Various machine learning models of Classification and Regression as above mentioned. After making models predict the whether patient suffering from heart disease or not. Making each model checked its accuracy through Accuracy Score metrics.