init

.gitignore

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+/.venv/
+/.idea/

Q1.py

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+from random import seed
+from random import randrange
+import random
+from csv import reader
+from tabulate import tabulate
+
+def load_csv(filename, skip = False):
+    dataset  = []
+    with open(filename, newline='') as file:
+        csv_reader = reader(file)
+        if skip:
+            next(csv_reader)
+        for row in csv_reader:
+            if row:
+                dataset.append(row)
+    return dataset
+
+
+def print_the_dataset(dataset, contents=True, length=True):
+    if (contents):
+        print(tabulate(dataset))
+
+    if (length):
+        print(len(dataset))
+
+
+def train_test_split(dataset, split):
+    # Create an empty list for the training set
+    train_set = []
+
+    # Define the size of the training set
+    train_size = int(split * len(dataset))
+
+    # Copy the original dataset to
+    dataset_copy = dataset.copy()
+
+    # Loops only to the size of the training set
+    while len(train_set) < train_size:
+        index = randrange(len(dataset_copy))
+        # Populate the training set, by moving the data points from the
+        # dataset/test set to the training set
+        train_set.append(dataset_copy.pop(index))
+
+    # Return both the training set and test set
+    return train_set, dataset_copy
+
+
+def k_fold_cross_validation(dataset, k):
+    n = len(dataset)  # Length of the dataset
+    fold_size = n // k  # Divide the length into smaller folds
+    folds = []  # Empty list of folds
+
+    # Shuffle the dataset
+    shuffled_dataset = dataset.copy()
+    random.shuffle(shuffled_dataset)
+
+    for i in range(k):
+        # Assign a start and end variables in respect to the fold size
+        start = i * fold_size
+        end = start + fold_size
+
+        # Generate all the test indices for the current fold
+        test_indices = shuffled_dataset[start:end]
+
+        # Generate all the train indices for the all other folds
+        train_indices = shuffled_dataset[:start] + shuffled_dataset[end:]
+
+        # Create a test set that is randomly populated via the test_indices
+        test_set = test_indices
+
+        # Create a train set that is randomly populated via the train_indices
+        train_set = train_indices
+
+        folds.append((train_set, test_set))
+
+    return folds
+
+seed(1)
+
+filename = 'big_heart.csv'
+
+dataset = load_csv(filename, skip = True)
+print_the_dataset(dataset)
+
+training, test = train_test_split(dataset, 0.8)
+
+print(len(training))
+
+print(len(test))
+
+k = 5  # Number of folds for cross-validation
+folds = k_fold_cross_validation(dataset, k)
+
+# Print the size of each fold
+for i, fold in enumerate(folds):
+    train_set, test_set = fold
+    print(f"Fold {i+1}: Training set size: {len(train_set)}, Test set size: {len(test_set)}")

Q2.py

@@ -0,0 +1,150 @@
+from math import sqrt
+from matplotlib import pyplot as plot
+from random import seed
+from random import randrange
+from csv import reader
+
+
+def load_csv(filename, skip=False):
+    dataset = list()
+    with open(filename, newline='') as file:
+        csv_reader = reader(file)
+        if skip:
+            next(csv_reader)
+        for row in csv_reader:
+            if not row:
+                continue
+            dataset.append(row)
+    return dataset
+
+
+def string_column_to_float(dataset, column):
+    for row in dataset:
+        # The strip() function remove white space
+        # then convert the data into a decimal number (float)
+        # and overwrite the original data
+        row[column] = float(row[column].strip())
+
+def mean(values):
+    mean_results = 0.0
+    mean_results = sum(values) / float(len(values))
+    return mean_results
+
+def regularisation(parameter, lambda_value=0.01):
+    parameter = parameter  * (1 - lambda_value)
+    return parameter
+
+
+def leastSquares(dataset):
+    x = list()
+    y = list()
+
+    for row in dataset:
+        x.append(row[0])
+
+    for row in dataset:
+        y.append(row[1])
+
+    b0 = 0
+    b1 = 0
+
+    # using the formula to calculate the b1 and b0
+    numerator = 0
+    denominator = 0
+
+    x_mean = mean(x)
+    y_mean = mean(y)
+
+    numerator = sum((x[i] - x_mean) * (y[i] - y_mean) for i in range(len(x)))
+    denominator = sum((x[i] - x_mean) ** 2 for i in range(len(x)))
+
+    b1 = numerator / denominator
+    b0 = y_mean - b1 * x_mean
+
+    return [b0, b1]
+
+def root_mean_square_error(actual, predicted):
+    rmse = 0.0
+    sum_error = 0.0
+    sum_error = sum((predicted[i] - actual[i]) ** 2 for i in range(len(actual)))
+    rmse = sqrt(sum_error / len(actual))
+    return rmse
+
+
+def simple_linear_regression(train, test):
+    predictions = list()
+    b0, b1 = leastSquares(train)
+
+    # Calculate the prediction (yhat)
+    for row in test:
+        yhat = b0 + b1 * row[0]
+        predictions.append(yhat)
+
+    return predictions
+
+
+def train_test_split(dataset, split):
+    train = list()
+    test = list(dataset)
+
+    train_size = int(split * len(dataset))
+    while len(train) < train_size:
+        index = randrange(len(test))
+        train.append(test.pop(index))
+
+    return train, test
+
+
+def evaluate_simple_linear_regression(dataset, split=0):
+    train, test = train_test_split(dataset, split)
+    test_set = list()
+
+    for row in test:
+        row_copy = list(row)
+        row_copy[-1] = None
+        test_set.append(row_copy)
+
+    predicted = simple_linear_regression(train, test_set)
+
+    actual = [row[-1] for row in test]
+
+    rmse = root_mean_square_error(actual, predicted)
+
+    return rmse
+
+
+def visualise_dataset(dataset):
+    test_set = list()
+
+    for row in dataset:
+        row_copy = list(row)
+        row_copy[-1] = None
+        test_set.append(row_copy)
+
+    sizes, prices = [], []
+    for i in range(len(dataset)):
+        sizes.append(dataset[i][0])
+        prices.append(dataset[i][1])
+
+    plot.figure()
+    plot.plot(sizes, prices, 'x')
+    plot.plot(test_set, simple_linear_regression(dataset, test_set))
+    plot.xlabel('Fertility rate')
+    plot.ylabel('Worker percent')
+    plot.grid()
+    plot.tight_layout()
+    plot.show()
+
+seed(1)
+
+filename = 'fertility_rate-worker_percent.csv'
+dataset = load_csv(filename, skip=True)
+
+for i in range(len(dataset[0])):
+    string_column_to_float(dataset, i)
+
+split = 0.6
+rmse = evaluate_simple_linear_regression(dataset, split)
+
+print('Root Mean Square Error: %.3f' % rmse)
+visualise_dataset(dataset)

big_heart.csv

@@ -0,0 +1,206 @@
+age,sex,cp,trestbps,chol,fbs,restecg,thalach,exang,oldpeak,slope,ca,thal,target
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readme.md

@@ -0,0 +1,24 @@
+Assignment 3
+
+Done: 
+
+---
+1. Q1
+2. Q2 
+
+---
+ToDo:
+1. Q3
+2. Q4
+---
+
+* Submit once after init run
+* Make changes
+* Resubmit
+* If full marks
+* * Fin
+* * Else
+* * Resubmit
+
+---
+<h1>Profit?</h1>