BATCH 16 - FORECASTING IPL SCORES USING MACHINE LEARNING

PROGRAMMING LANGUAGE SELECTION

Python is dynamic, object oriented and multipurpose programming language. It has a huge and inclusive standard library. It helps its user express concepts in fewer lines of code which is not possible for languages such as in C++ and java. It is extended to create WEB applications as well. Since the project we have worked majorly deals with machine learning and data analysis, python is one of the best choices we could make use of because of its dynamic nature.

HTML is hypertext markup language. It is language that helps in creation of web pages in the internet. We have used HTML language to create GUI for user interactions, where users input the values for specified features and obtain their results.

CSS is known as cascading style sheets. They create a uniform look across several pages of a website. It is used to describe how html elements are to be displayed on the screen. It plays a key role in the web designing.

PLATFORM SELECTION Our project was developed in Windows 10. This OS was our choice given that our data needed a lot of fine tuning and visualizations which could be easily provided in the MS Excel software. Windows provide GUI for its computers. Windows minimizes the need for commands to operate OS by simply implementing mouse which navigates through menus, tabs, dialog boxes and icons. Windows was given this name due to its dynamic nature to allow multiple tasks to run at the same time. We have chosen this platform since its user friendly, since our project required more of analytics rather than focus on a very powerful development environment.

GRAPHICAL USER INTERFACE The success of any project depends on how well it serves the end users. The main users of our system include doctors or patients who have little/no knowledge of the implementations. Thus, we require a friendly user interface including text boxes, buttons, labels etc. The web page is developed in HTML and styles with CSS. Users are provided with outputs in terms of images, tables and graphs as well to make the results more comprehensive.

Flask is a web framework coded in python it uses template jinja2. Flask is used to build WEB applications such as web pages, blogs, or a website. Our first choice of frame work was flask given its syntax that is very similar to python itself, and the ease to learn the framework. Flask has it dependencies as follows: • WSGI which is a utility library. • Jinja2 which is template engine.

PYTHON LIBRARIES • Scikit-learn: scikit learn is a wide python library which practices machine learning, cross validation of data, and preprocessing of data. It is built on NumPy and SciPy. • Pandas: Pandas is a python package. It provides a designed data structure with fast expressive and flexible features. It makes the data work easier and intuitive. For many different kinds of data pandas is well suited such as tabular, matrix, ordered or unordered data. • NumPy: NumPy’s main object is a multidimensional array, it holds an array data structure. NumPy is a core python library which contains a collection of tools and techniques. One of these tools is multi-dimensional object. • Matplotlib: It is a library extensively used for visualizing the data.

IMPLEMENTATION STEPS

1. Define a module for loading the .csv file. Check for rows with a few missing values and fill them with the mode value of each feature. Analyze the data statistically, which involves plotting the histograms and chi-square test for normality.
2. Define functions for feature selection and extraction which is based on Recursive Feature Elimination. Keeping only high-performance features.
3. Evaluate the regressor using the metrics such as RMSE, MSE, MAE and accuracy. The best regressor is to be used for pred.
4. Develop a Score prediction tool using the best performing regression to predict scores using the current match conditions.
5. Check for consistency among the most important features and find out the covariance matrix of the top features to check if the features are statistically independent.
6. Develop a graphical user interface to enhance the experience of the end users in obtaining

STEPS TO RUN THE PROJECT . Download the file conatined in the CD. . Open terminal and install the following python modules . pip install flask . pip install pandas . pip install python-dotenv . Finally, run the project by - . python main.py . The web-page will be available at http://localhost:5000