An Optimisation Project in Python. Understanding trends in Football Transfers and building a regression model to predict the market value of players.
Goal: To understand the trend of how much clubs have been paying over the market value for players over the past 14 seasons.
- Transfers.csv - The CSV file that contains the full Kaggle dataset.
- Calculating_Difference.py - The python file that calculates the Difference metric and the Average Difference.
- Mean_Table.csv is created.
- Line_Graph.png - Image of the line graph
- Plot_Difference.py - Python file where the line graph for Average Difference is plotted.
- import pandas as pd
- import numpy as np
- import matplotlib.pyplot as plt
Goal: To create a Linear Regression model that can accurately predict the Market Value of players in the coming seasons.
- Transfers.csv - The CSV file that contains the full Kaggle dataset.
- Plot_Regression.py - Python file for creating the full Linear Regression model.
- Actual_Predicted.csv is created.
- Linear_Regression.png - Image of the linear regression model and scatter plot
- import numpy as np
- import pandas as pd
- import matplotlib.pyplot as plt
- from sklearn.model_selection import train_test_split
- from sklearn.linear_model import LinearRegression
- from sklearn import metrics