Alankar11

This project explores and analyzes a real estate dataset to understand the key factors that influence house prices. By performing Exploratory Data Analysis (EDA), we uncover trends, relationships, and patterns in housing features, amenities, time of sale, and neighborhood characteristics. The goal is to generate insights that support better decision-making in property valuation, marketing strategies, and predictive modeling. This EDA helped uncover pricing patterns, feature importance, and market segmentation in the housing dataset. These insights can support: Better predictive models Targeted pricing strategies Informed real estate investment decisions

The goal of this project was to clean, and prepare raw data to enable accurate analysis and decision-making. The project focused on handling incomplete, inconsistent, and unstructured data to make it ready for further exploration and modeling. Handled missing values using imputation techniques (mean, median, mode) or deletion where necessary. Standardized data formats (date-time, currency, categorical variables) for consistency. Removed duplicates and corrected inconsistencies in entries. Performed data transformations, including normalizing, encoding categorical variables, and aggregating datasets for deeper insights. Tools & Technologies used- Python, Excel.

The IMDB Movie Analysis project is a statistical exploration of various factors influencing movie ratings and profitability. It includes genre analysis, revealing that popular genres like Action, Comedy, and Drama are most frequently produced, with ratings generally consistent across genres. Movie duration analysis shows that most films range between 100–150 minutes, and those with longer durations tend to have slightly higher ratings. The language analysis indicates that while English dominates in volume, lesser-known languages like Telugu and Polish receive higher average ratings. The director analysis ranks directors based on average IMDB scores, highlighting the top performers. Lastly, the budget analysis finds a weak correlation between budget and gross income, suggesting that higher investment does not guarantee higher earnings, although some high-budget films still achieve significant profit margins. The IMDB Movie Analysis project is a statistical exploration of various factors influencing movie ratings and profitability. It includes genre analysis, revealing that popular genres like Action, Comedy, and Drama are most frequently produced, with ratings generally consistent across genres. Movie duration analysis shows that most films range between 100–150 minutes, and those with longer durations tend to have slightly higher ratings. The language analysis indicates that while English dominates in volume, lesser-known languages like Telugu and Polish receive higher average ratings. The director analysis ranks directors based on average IMDB scores, highlighting the top performers. Lastly, the budget analysis finds a weak correlation between budget and gross income, suggesting that higher investment does not guarantee higher earnings, although some high-budget films still achieve significant profit margins. Skills: Microsoft Excel · Microsoft PowerPoint · Data Analysis · Data Analytics · Data Modeling · Data Science · Data Visualization · Statistical Data Analysis

The Instagram User Analytics project using MySQL focuses on analyzing user behavior and engagement patterns on the platform. It involves extracting, querying, and processing data related to user activity such as post frequency, likes, comments, follower growth, and content interaction. Key metrics like average engagement rate, most active users, and peak posting times are calculated through SQL queries. The project helps in identifying trends, influential users, and content performance, ultimately providing insights to enhance social media strategies and user targeting based on data-driven decisions. The Instagram User Analytics project using MySQL focuses on analyzing user behavior and engagement patterns on the platform. It involves extracting, querying, and processing data related to user activity such as post frequency, likes, comments, follower growth, and content interaction. Key metrics like average engagement rate, most active users, and peak posting times are calculated through SQL queries. The project helps in identifying trends, influential users, and content performance, ultimately providing insights to enhance social media strategies and user targeting based on data-driven decisions. Skills: Microsoft Excel · SQL · MySQL · Microsoft PowerPoint · Data Analysis · Data Analytics · Data Modeling · Data Science · Statistical Data Analysis · Data Visualization