Forecasting_Cardiovascular_Ailments_usingMachine_Learning

import numpy as np import pandas as pd import tensorflow as tf from sklearn.model_selection import train_test_split from sklearn.preprocessing import StandardScaler import seaborn as sns import matplotlib.pyplot as plt from sklearn.preprocessing import scale # scale and center data

from sklearn.model_selection import GridSearchCV # this will do cross validation from sklearn.decomposition import PCA # to perform PCA to plot the data

from sklearn.metrics import accuracy_score, confusion_matrix, classification_report

import os from google.colab import drive drive.mount('/content/MyDrive/', force_remount=True)

import numpy as np import pandas as pd import tensorflow as tf from sklearn.model_selection import train_test_split from sklearn.preprocessing import StandardScaler import seaborn as sns import matplotlib.pyplot as plt

d_df = pd.read_csv("./MyDrive/MyDrive/Heart_Disease_Prediction/heart_disease.csv")

OR - pd.read_csv(data_path+"deliveries.csv"), where data_path = "../input/"

reading deliveries dataset

''''score_df''' m_df = pd.read_csv("MyDrive/MyDrive/Heart_Disease_Prediction/heart_disease.csv")

OR - pd.read_csv(data_path+"matches.csv"), where data_path = "../input/"

reading matches dataset

csv- Comma seperated values

d_df.head()

m_df.head()

d_df.info()

m_df.info()

d_df.describe()

m_df.describe()

Load the heart disease dataset

data = pd.read_csv("./MyDrive/MyDrive/Heart_Disease_Prediction/heart_disease.csv")

Split the data into features and target

X = data.iloc[:, :-1] y = data.iloc[:, -1]

Split the data into train and test sets

X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=42)

Scale the data

scaler = StandardScaler() X_train = scaler.fit_transform(X_train) X_test = scaler.transform(X_test)

Define the model

model = tf.keras.Sequential([ tf.keras.layers.Dense(64, activation='relu', input_shape=(X_train.shape[1],)), tf.keras.layers.Dropout(0.2), tf.keras.layers.Dense(32, activation='relu'), tf.keras.layers.Dropout(0.2), tf.keras.layers.Dense(1, activation='sigmoid') ])

Compile the model

model.compile(optimizer='adam', loss='binary_crossentropy', metrics=['accuracy'])

Train the model

model.fit(X_train, y_train, epochs=50, batch_size=32, validation_split=0.2)

Evaluate the model on the test set

loss, accuracy = model.evaluate(X_test, y_test) print("Test loss:", loss) print("Test accuracy:", accuracy)

import pandas as pd import seaborn as sns import matplotlib.pyplot as plt

Create a DataFrame

data = pd.DataFrame({'sex': ['M', 'F', 'M', 'F', 'M'], 'target': [1, 0, 1, 0, 1]})

Group the data by sex and target and count the occurrences

grouped = data.groupby(['sex', 'target']).size().reset_index(name='count')

Create a crosstab of sex and target

crosstab = pd.crosstab(grouped['sex'], grouped['target'])

Create a heatmap

sns.heatmap(crosstab, annot=True, fmt="d", cmap="Blues") plt.xlabel('Target') plt.ylabel('Sex') plt.title('Heart Disease Cases by Sex') plt.show()

d_df['target'].count()

sns.countplot(x='target',data=m_df)

import matplotlib.pyplot as plt

Group the data by sex and target

grouped = data.groupby(['sex', 'target']).size().reset_index(name='count')

Create a bar chart

fig, ax = plt.subplots() ax.bar(grouped['sex'].astype(str) + '-' + grouped['target'].astype(str), grouped['count']) ax.set_xlabel('Sex-Target') ax.set_ylabel('Count') ax.set_title('Heart Disease Cases by Sex') plt.show()

m_df.hist(figsize=(14,14) , color = 'lightblue') plt.show()