Data analysis is an important aspect in this technological bloom. There are tons of data being produced every day. If we can use this data efficiently, we can surely benefit from it. Not to mention, if the data of activity of people in the online business is tracked down, companies can focus more on the hot leads and therefore, have a policy for the hot leads to convert them into customers. The purpose of this project is to analyze the data to generate lead score and predict the potential costumers on the basis of the lead score. In the B2B realm alone, MarketingSherpa calculates that only around 21% of companies have established a lead scoring practice. Thus, we try to bridge that gap using Lead Score prediction along with visualizations using logistic regression for analyzing the data. This project Builds a logistic regression model to assign a lead score between 0 and 100. A higher score would mean that the lead is hot, i.e., is most likely to convert whereas a lower score would mean that the lead is cold and will mostly not get converted. We managed to get insight of the customer lead score with more than 85% accuracy. The project can be further implemented in the dataset of similar attributes.