Upload 4 files

app.py

@@ -0,0 +1,50 @@
+import pandas as pd
+import numpy as np
+import streamlit as st
+import seaborn as sns
+import matplotlib.pyplot as plt
+from sklearn.linear_model import LinearRegression
+from sklearn.model_selection import train_test_split
+from sklearn.metrics import mean_squared_error, r2_score
+
+# Load the data
+data = pd.read_csv('data.csv')
+
+
+# Train the linear regression model
+X = data['Hours'].values.reshape(-1, 1)
+y = data['Scores'].values.reshape(-1, 1)
+X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=42)
+
+model = LinearRegression()
+model.fit(X_train, y_train)
+
+# Make predictions
+y_pred = model.predict(X_test)
+
+# Compute metrics
+mse = mean_squared_error(y_test, y_pred)
+r2 = r2_score(y_test, y_pred)
+
+# Create the web app
+st.title('Student Score Prediction')
+st.write('Enter the number of study hours:')
+hours = st.number_input('', min_value=0, max_value=24, step=1)
+score = int(model.predict([[hours]]))
+if score > 100:
+    score = 100
+
+st.write(f'Predicted Score: {score} Marks if he studies {hours} hours')
+
+
+# Perform EDA
+# st.title('Exploratory Data Analysis')
+# st.write('Data Summary:')
+# st.write(data.describe())
+
+st.write('Scatter Plot:')
+fig, ax = plt.subplots(figsize=(10, 6))
+sns.scatterplot(data=data, x='Hours', y='Scores')
+plt.xlabel('Hours')
+plt.ylabel('Scores')
+st.pyplot(fig)

data.csv

@@ -0,0 +1,26 @@
+Hours,Scores
+2.5,21
+5.1,47
+3.2,27
+8.5,75
+3.5,30
+1.5,20
+9.2,88
+5.5,60
+8.3,81
+2.7,25
+7.7,85
+5.9,62
+4.5,41
+3.3,42
+1.1,17
+8.9,95
+2.5,30
+1.9,24
+6.1,67
+7.4,69
+2.7,30
+4.8,54
+3.8,35
+6.9,76
+7.8,86

requirements.txt

@@ -0,0 +1,6 @@
+streamlit
+pandas
+numpy
+seaborn
+matplotlib
+scikit-learn