I am trying to predict 'Full_Time_Home_Goals'
My code is:
import pandas as pd
from sklearn.model_selection import train_test_split
from sklearn.tree import DecisionTreeRegressor
from sklearn.metrics import mean_absolute_error
from sklearn.ensemble import RandomForestRegressor
import os
import xlrd
import datetime
import numpy as np
# Set option to display all the rows and columns in the dataset. If there are more rows, adjust number accordingly.
pd.set_option('display.max_rows', 5000)
pd.set_option('display.max_columns', 500)
pd.set_option('display.width', 1000)
# Pandas needs you to define the column as date before its imported and then call the column and define as a date
# hence this step.
date_col = ['Date']
df = pd.read_csv(
r'C:\Users\harsh\Documents\My Dream\Desktop\Machine Learning\Attempt1\Historical Data\Concat_Cleaned.csv'
, parse_dates=date_col, skiprows=0, low_memory=False)
# Converting/defining the columns
# Before you define column types, you need to fill all NaN with a value. We will be reconverting them later
df = df.fillna(101)
# Defining column types
convert_dict = {'League_Division': str,
'HomeTeam': str,
'AwayTeam': str,
'Full_Time_Home_Goals': int,
'Full_Time_Away_Goals': int,
'Full_Time_Result': str,
'Half_Time_Home_Goals': int,
'Half_Time_Away_Goals': int,
'Half_Time_Result': str,
'Attendance': int,
'Referee': str,
'Home_Team_Shots': int,
'Away_Team_Shots': int,
'Home_Team_Shots_on_Target': int,
'Away_Team_Shots_on_Target': int,
'Home_Team_Hit_Woodwork': int,
'Away_Team_Hit_Woodwork': int,
'Home_Team_Corners': int,
'Away_Team_Corners': int,
'Home_Team_Fouls': int,
'Away_Team_Fouls': int,
'Home_Offsides': int,
'Away_Offsides': int,
'Home_Team_Yellow_Cards': int,
'Away_Team_Yellow_Cards': int,
'Home_Team_Red_Cards': int,
'Away_Team_Red_Cards': int,
'Home_Team_Bookings_Points': float,
'Away_Team_Bookings_Points': float,
}
df = df.astype(convert_dict)
# Reverting the replace values step to get original dataframe and with the defined filetypes
df = df.replace('101', np.NAN, regex=True)
df = df.replace(101, np.NAN, regex=True)
# Exploration
print(df.dtypes)
print(df)
# Clean dataset by dropping null rows
data = df.dropna(axis=0)
# Column that you want to predict = y
y = df.Full_Time_Home_Goals
# Columns that are inputted into the model to make predictions (dependants), Cannot be column y
features = ['HomeTeam', 'AwayTeam', 'Full_Time_Away_Goals', 'Full_Time_Result']
# Create X
X = df[features]
# Split into validation and training data
train_X, val_X, train_y, val_y = train_test_split(X, y, random_state=1)
# Specify Model
soccer_model = DecisionTreeRegressor(random_state=1)
# Fit Model
soccer_model.fit(train_X, train_y)
I am getting an error fitting to the model
# Fit Model
soccer_model.fit(train_X, train_y)
Throws me an error:
ValueError: could not convert string to float: "Nott'm Forest"
How can I solve this and run the model to get the output? I tried to follow a few examples but I am unable to progress.
You can fine the example concat_cleaned file here
You have to transform your categorical data into numerical data. For that, you could use the OneHotEncoder:
import os
import xlrd
import datetime
import numpy as np
from sklearn.tree import DecisionTreeRegressor
from sklearn.preprocessing import OneHotEncoder
# Set option to display all the rows and columns in the dataset. If there are more rows, adjust number accordingly.
pd.set_option('display.max_rows', 5000)
pd.set_option('display.max_columns', 500)
pd.set_option('display.width', 1000)
# Pandas needs you to define the column as date before its imported and then call the column and define as a date
# hence this step.
date_col = ['Date']
df = pd.read_csv(
r'Concat_Cleaned_Example.csv'
, parse_dates=date_col, skiprows=0, low_memory=False)
# Converting/defining the columns
# Before you define column types, you need to fill all NaN with a value. We will be reconverting them later
df = df.fillna(101)
# Defining column types
convert_dict = {'League_Division': str,
'HomeTeam': str,
'AwayTeam': str,
'Full_Time_Home_Goals': int,
'Full_Time_Away_Goals': int,
'Full_Time_Result': str,
'Half_Time_Home_Goals': int,
'Half_Time_Away_Goals': int,
'Half_Time_Result': str,
'Attendance': int,
'Referee': str,
'Home_Team_Shots': int,
'Away_Team_Shots': int,
'Home_Team_Shots_on_Target': int,
'Away_Team_Shots_on_Target': int,
'Home_Team_Hit_Woodwork': int,
'Away_Team_Hit_Woodwork': int,
'Home_Team_Corners': int,
'Away_Team_Corners': int,
'Home_Team_Fouls': int,
'Away_Team_Fouls': int,
'Home_Offsides': int,
'Away_Offsides': int,
'Home_Team_Yellow_Cards': int,
'Away_Team_Yellow_Cards': int,
'Home_Team_Red_Cards': int,
'Away_Team_Red_Cards': int,
'Home_Team_Bookings_Points': float,
'Away_Team_Bookings_Points': float,
}
df = df.astype(convert_dict)
# Reverting the replace values step to get original dataframe and with the defined filetypes
df = df.replace('101', np.NAN, regex=True)
df = df.replace(101, np.NAN, regex=True)
# Clean dataset by dropping null rows
data = df.dropna(axis=0)
# Column that you want to predict = y
y = df.Full_Time_Home_Goals
# Columns that are inputted into the model to make predictions (dependants), Cannot be column y
features = ['HomeTeam', 'AwayTeam', 'Full_Time_Away_Goals', 'Full_Time_Result']
# Create X
X = df[features]
# Split into validation and training data
train_X, val_X, train_y, val_y = train_test_split(X, y, random_state=1)
# Specify Model
soccer_model = DecisionTreeRegressor(random_state=1)
# Define and train OneHotEncoder to transform numerical data to a numeric array
enc = OneHotEncoder(handle_unknown='ignore')
enc.fit(train_X)
transformed_train_X = enc.transform(train_X)
# Fit Model
soccer_model.fit(transformed_train_X, train_y)
That way your data, for instance (Man United,Newcastle,0,H) would be encoded as
(0, 14) 1.0
(0, 35) 1.0
(0, 43) 1.0
(0, 50) 1.0
You can have a look at it for any data point to verify that it is correctly encoded, by using:
entry_id = 1
print(transformed_train_X[entry_id])
for i in range(0,transformed_train_X[0].shape[1]):
if(transformed_train_X[entry_id,i]==1.0):
print(enc.get_feature_names()[i])
Output:
(0, 14) 1.0
(0, 35) 1.0
(0, 43) 1.0
(0, 50) 1.0
x0_Man United
x1_Newcastle
x2_0
x3_H