Abhilash Ky

Organized and dependable candidate successful at managing multiple priorities with a positive attitude. Willingness to take on added responsibilities to meet team goals.

PROJECTS Project1 : Glass Type Prediction using Classification algorithms Project2 : Global Power Plant Database prediction 1. Primary Fuel 2. Capacity_mw using regression and classification algorithms. Automation Project: Using BeautifulSoup (BS4) and Selenium tool

• Analyzed large datasets to extract valuable insights.

• Conducted extensive data preprocessing to ensure data quality and relevance.

• Implemented various classification models, including Random Forest, Logistic Regression, Support Vector Machine, and Naive Bayes.

• Delivered a robust and accurate glass type prediction system as a result of the project.

• Data Preparation: Gathered and cleaned the Global Power Plant Database, ensuring accurate and relevant information on primary fuel types and power plant capacities.

• Feature Engineering: Identifying and creating relevant features for the regression model, such as the type of primary fuel for classification and the capacity in megawatts for regression.

• Regression Model: Implemented a regression algorithm (e.g., linear regression, decision tree regression) to predict power plant capacities based on selected features.

• Classification Model: Employed a classification algorithm (e.g., logistic regression, decision tree classifier) to predict the primary fuel type of power plants based on relevant features.

• Evaluation and Fine-Tuning: Assess the performance of both regression and classification models using appropriate metrics (e.g., Mean Squared Error for regression, accuracy for classification).

• Written a BeautifulSoup framework on dineout.co.in for extracting the data of hotels, cuisines, location, ratings and image URL and stored into DataFrame and saved the file in the form of excel sheet us using pandas

• Written Selenium automation framework to search for sneakers in flipkart.com and scrape data for 100 shoes and make a dataframe and save the file in the excel format