CWA2024/playTennis_list.py at main · Peter-Barry/CWA2024 · GitHub

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import pandas as pd
from sklearn.model_selection import train_test_split
from sklearn.linear_model import LogisticRegression
from sklearn.metrics import accuracy_score

# Load the dataset
data = {
    'Day': [1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14],
    'Outlook': ['Sunny', 'Overcast', 'Overcast', 'Rain', 'Rain', 'Rain', 'Overcast', 'Overcast', 'Overcast', 'Rain', 'Sunny', 'Overcast', 'Overcast', 'Rain'],
    'Temp': ['Hot', 'Hot', 'Hot', 'Mild', 'Cool', 'Cool', 'Cool', 'Mild', 'Cool', 'Mild', 'Mild', 'Mild', 'Hot', 'Mild'],
    'Humidity': ['High', 'High', 'High', 'High', 'Normal', 'Normal', 'Normal', 'High', 'Normal', 'Normal', 'Normal', 'High', 'Normal', 'High'],
    'Wind': ['Weak', 'Strong', 'Weak', 'Weak', 'Weak', 'Strong', 'Weak', 'Weak', 'Weak', 'Weak', 'Strong', 'Strong', 'Weak', 'Strong'],
    'Play': ['No', 'No', 'Yes', 'Yes', 'Yes', 'No', 'Yes', 'No', 'Yes', 'Yes', 'Yes', 'Yes', 'Yes', 'No']
}

df = pd.DataFrame(data)

# One-hot encode categorical variables
df_encoded = pd.get_dummies(df[['Outlook', 'Temp', 'Humidity', 'Wind']])

# Concatenate encoded features with original dataframe
df = pd.concat([df[['Day']], df_encoded, df['Play']], axis=1)

# Split the dataset into features (X) and target variable (Y)
X = df.drop('Play', axis=1)
Y = df['Play']

# Split the dataset into training and testing sets
X_train, X_test, Y_train, Y_test = train_test_split(X, Y, test_size=0.2, random_state=42)

# Create a logistic regression model
model = LogisticRegression()

# Fit the model to the training data
model.fit(X_train, Y_train)

# Make predictions on the test data
predictions = model.predict(X_test)

# Evaluate the model
accuracy = accuracy_score(Y_test, predictions)
print(f'Accuracy: {accuracy}')
import pandas as pd
from sklearn.model_selection import train_test_split
from sklearn.linear_model import LogisticRegression
from sklearn.metrics import accuracy_score

# Load the dataset
data = {
    'Day': [1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14],
    'Outlook': ['Sunny', 'Overcast', 'Overcast', 'Rain', 'Rain', 'Rain', 'Overcast', 'Overcast', 'Overcast', 'Rain', 'Sunny', 'Overcast', 'Overcast', 'Rain'],
    'Temp': ['Hot', 'Hot', 'Hot', 'Mild', 'Cool', 'Cool', 'Cool', 'Mild', 'Cool', 'Mild', 'Mild', 'Mild', 'Hot', 'Mild'],
    'Humidity': ['High', 'High', 'High', 'High', 'Normal', 'Normal', 'Normal', 'High', 'Normal', 'Normal', 'Normal', 'High', 'Normal', 'High'],
    'Wind': ['Weak', 'Strong', 'Weak', 'Weak', 'Weak', 'Strong', 'Weak', 'Weak', 'Weak', 'Weak', 'Strong', 'Strong', 'Weak', 'Strong'],
    'Play': ['No', 'No', 'Yes', 'Yes', 'Yes', 'No', 'Yes', 'No', 'Yes', 'Yes', 'Yes', 'Yes', 'Yes', 'No']
}

df = pd.DataFrame(data)

# One-hot encode categorical variables
df_encoded = pd.get_dummies(df[['Outlook', 'Temp', 'Humidity', 'Wind']])

# Concatenate encoded features with original dataframe
df = pd.concat([df[['Day']], df_encoded, df['Play']], axis=1)

# Split the dataset into features (X) and target variable (Y)
X = df.drop('Play', axis=1)
Y = df['Play']

# Split the dataset into training and testing sets
X_train, X_test, Y_train, Y_test = train_test_split(X, Y, test_size=0.2, random_state=42)

# Create a logistic regression model
model = LogisticRegression()

# Fit the model to the training data
model.fit(X_train, Y_train)

# Make predictions on the test data
predictions = model.predict(X_test)

# Evaluate the model
accuracy = accuracy_score(Y_test, predictions)
print(f'Accuracy: {accuracy}')

def interpret_mse(accuracy):
    if accuracy < 10:
        return "Excellent model accuracy. This is the SHIZ."
    elif accuracy < 20:
        return "Good model accuracy. A fine auld model."
    elif accuracy < 30:
        return "Average model accuracy. That'll do pig."
    elif accuracy < 40:
        return "Below average model accuracy. Don't bet your house on this being true."
    else:
        return "Poor model accuracy! Get better data or try another fit like polyfit. This shirt ain't linear. \n"

mse_remark = interpret_mse(accuracy)
print(mse_remark)