Stock price forecasting is a popular and important topic in financial and academic studies. Share Market is an untidy place for predicting since there are no significant rules to estimate or predict the price of share in the share market. Many methods like technical analysis, fundamental analysis, time series analysis and statistical analysis, etc. are all used to attempt to predict the price in the share market. In this project we attempt to implement a Predictive Modeling and Technical Indicators Analysis approach to predict stock market prices by developing an automated stock data collection and predictive analysis tool. Predictive Modeling is very effectively implemented in forecasting stock prices, returns, and stock modeling and the most frequent methodologies are the Decision Tree algorithm and the Regression Algorithm. This project is for Indian users as the prediction is done on the listed companies of National Stock Exchange of India’s NIFTY index. We outline the design of the Predictive models with its salient features and customizable parameters, and design visually interactive trend charts for stock technical indicators analysis. We select a certain group of parameters with relatively significant impact on the share price of a company. With the help of statistical analysis, the relation between the selected factors and share price is formulated which can help in forecasting accurate results. Although, share market can never be predicted, due to its vague domain, this project aims at applying Predictive Modeling Machine Learning techniques and stock indicator concepts in forecasting the stock prices. Demo of the tool developed in this project available here: https://youtu.be/VBx7Ik6aw7c **Process Implementation Flow Diagram:** ![enter image description here][1] [1]: http://www.madhurikabachelorette.co.in/images/process_implementation_flow_diagram.png