The Stock Market (SM) prediction price is one of an interesting field at present ‎because it is a chaotic, non-linear, dynamic, non-stationary, noisy and quite ‎difficult. Data mining has been effectively used in stock predicting hence ‎researchers have explored Technical Indicators (TIs) to optimize the parameters. ‎The main objective of this project is to predict Iraq stock market and improve the ‎prediction using TIs.‎<br />The proposed model consists of three major stages; the first stage is preprocessing ‎data that focuses on preparing the data for mining process. It includes features ‎extraction; transform nominal to numeric; and interpolation. The second stage ‎involves building prediction model using Decision Tree (DT) classifier and finally, ‎the evaluation in third stage has been performed depending on popular measures ‎of prediction and 10-Cross Validation (CV). Confusion Matrix parameters: Accuracy, ‎Precision, Recall, F1-measure, and Specificity measures are used to evaluate DT ‎model.‎<br />The model is applied on local SM specifically is (Asia company), which is built and ‎structured. Finally, a comparison has been implemented on input features with ‎and without TIs. The results show using TIs satisfy better results of prediction. The ‎Accuracy rate is raised from 64% of standard features only to 92% of proposed ‎approach, which depends on TIs.‎<br />Business intelligence (BI), and specifically financial analysis, is the focus of this ‎project. The SM prediction is a hot area of research for scholars and inventors. ‎This is because the SM prediction price still represents an issue in the financial ‎time series that needs to be addressed. SM is an effective part of any country's ‎economy. It plays a significant role in the industry and this, in turn, affects the ‎country's economy growth. SM is a public place where companies are allowed to ‎trade money through the sale or purchase of shares and stocks after determining ‎the price agreed upon in advance. Because the connection between inputs and ‎outputs are variable, nonlinear, and volatile in nature, the estimation of stock ‎market future values has become a hard task. Choosing a suitable training and ‎prediction method is also still a very critical problem. The SM prediction is a ‎process of concluding the future value of a stock's company based on its historical ‎data. The first objective of this thesis at predicting the stock market prices as ‎research of this context is scares. Two ways can be followed to meet this aim. ‎First, fundamental analysis must be determined by mathematical data of a ‎particular company. Second, technical analysis can be conducted by Technical ‎Indicators (TIs) and Machine Learning (ML). Numerical factors such as daily ups ‎and downs, the volume of stock, tendency pointers, the highest and lowest prices ‎of a day, directories, simple moving average, etc. can be used. Previous literature ‎has attempted to discover some accurate arithmetic models that can allocate ‎these inputs to predict the desirable outputs.‎<br /><br />Recommendations ‎<br />This research aims to design a prediction system to provide clear guidance for ‎fund managers and individual investors. ‎<br />‎1.‎ The proposed system can lead to choosing the right strategies for making ‎investment decisions, and this, in turn, can enhance revenue during the ‎uncertainty periods. This will have achieved through three objectives:‎<br />‎2.‎ Improving the prediction of general index price, daily stock price, and daily ‎expected return in the SM.‎<br />‎3.‎ Increasing the accuracy in the decision-making process (selling, buying, and ‎waiting) stocks.‎<br />"AL_mustaqbal University is the first university in Iraq"