Fitness and health tracker added

Fitness and health tracker/app.py

@@ -0,0 +1,97 @@
+import streamlit as st
+import pandas as pd
+import matplotlib.pyplot as plt
+import seaborn as sns
+from sklearn.linear_model import LinearRegression
+from sklearn.metrics import mean_squared_error
+from sklearn.cluster import KMeans
+
+# Load dataset
+df = pd.read_csv('fitness_health_data.csv')
+
+# Define pages
+def home():
+    st.title("Fitness and Health Tracker")
+    st.write("This application provides insights into users' fitness and health data through Exploratory Data Analysis (EDA) and Machine Learning algorithms.")
+
+def eda():
+    st.title("Exploratory Data Analysis")
+
+    st.write("## Dataset")
+    st.write(df.head())
+
+    st.write("## Descriptive Statistics")
+    st.write(df.describe())
+
+    st.write("## Histograms")
+    fig, ax = plt.subplots()
+    df.hist(bins=20, figsize=(15, 10), ax=ax)
+    plt.tight_layout()
+    st.pyplot(fig)
+
+    st.write("## Pair Plot")
+    sns.pairplot(df)
+    st.pyplot()
+
+    st.write("## Box Plot")
+    fig, ax = plt.subplots()
+    sns.boxplot(data=df, ax=ax)
+    plt.xticks(rotation=90)
+    st.pyplot(fig)
+
+    st.write("## Correlation Matrix Heatmap")
+    corr_matrix = df.corr()
+    fig, ax = plt.subplots()
+    sns.heatmap(corr_matrix, annot=True, cmap='coolwarm', ax=ax)
+    plt.title('Correlation Matrix Heatmap')
+    st.pyplot(fig)
+
+def calories_prediction():
+    st.title("Calories Burned Prediction")
+
+    st.write("## Input Features")
+    steps = st.number_input('Steps', min_value=0, value=5000)
+    distance = st.number_input('Distance (km)', min_value=0.0, value=4.0)
+    active_minutes = st.number_input('Active Minutes', min_value=0, value=60)
+
+    features = ['Steps', 'Distance', 'Active_Minutes']
+    target = 'Calories'
+    X = df[features]
+    y = df[target]
+    model = LinearRegression()
+    model.fit(X, y)
+
+    input_data = pd.DataFrame([[steps, distance, active_minutes]], columns=features)
+    prediction = model.predict(input_data)[0]
+
+    st.write(f"## Predicted Calories Burned: {prediction:.2f}")
+    st.write(f"## Model Coefficients: {model.coef_}")
+    st.write(f"## Mean Squared Error: {mean_squared_error(y, model.predict(X)):.2f}")
+
+def heart_rate_clustering():
+    st.title("Heart Rate Clustering")
+
+    heart_rate = df[['User_ID', 'Heart_Rate']].groupby('User_ID').mean().reset_index()
+    kmeans = KMeans(n_clusters=3, random_state=42)
+    heart_rate['Cluster'] = kmeans.fit_predict(heart_rate[['Heart_Rate']])
+
+    fig, ax = plt.subplots()
+    plt.scatter(heart_rate['User_ID'], heart_rate['Heart_Rate'], c=heart_rate['Cluster'], cmap='viridis')
+    plt.xlabel('User ID')
+    plt.ylabel('Average Heart Rate')
+    plt.title('Heart Rate Clustering')
+    st.pyplot(fig)
+
+# Streamlit navigation
+pages = {
+    "Home": home,
+    "EDA": eda,
+    "Calories Prediction": calories_prediction,
+    "Heart Rate Clustering": heart_rate_clustering,
+}
+
+st.sidebar.title("Navigation")
+selection = st.sidebar.radio("Go to", list(pages.keys()))
+
+page = pages[selection]
+page()