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Browse files- app.py +79 -0
- requirements.txt +5 -0
- software_project_data.csv +301 -0
app.py
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import pandas as pd
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import numpy as np
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from sklearn.linear_model import LinearRegression
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from sklearn.model_selection import train_test_split
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from sklearn.metrics import mean_squared_error, r2_score
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import streamlit as st
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import altair as alt
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try:
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# Load the data
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df = pd.read_csv("software_project_data.csv")
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except FileNotFoundError:
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st.write("Error: Data file not found.")
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st.stop()
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# Prepare the data for the model
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X = df[['Project_Size', 'Num_Developers', 'Complexity']]
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y = df['Completion_Time']
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# Split the data into training and testing sets
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X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=42)
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# Create and train a Linear Regression model
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model = LinearRegression()
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model.fit(X_train, y_train)
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# Make predictions on the testing set
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y_pred = model.predict(X_test)
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# Evaluate the model's performance
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mse = mean_squared_error(y_test, y_pred)
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r2 = r2_score(y_test, y_pred)
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# Create a Streamlit app
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st.title("Software Project Completion Time Estimator")
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# Create tabs
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tab1, tab2, tab3 = st.tabs(["Data Visualization", "Model Performance", "Prediction"])
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# Tab 1: Data Visualization
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with tab1:
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st.write("### Software Project Data")
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st.write(df)
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# Scatter plot
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st.write("### Scatter Plot of Project Variables vs Completion Time")
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for col in ['Project_Size', 'Num_Developers', 'Complexity']:
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st.write(f"**{col} vs Completion Time**")
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st.altair_chart(
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alt.Chart(df).mark_circle().encode(
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x=col,
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y='Completion_Time',
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tooltip=[col, 'Completion_Time']
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).interactive(),
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use_container_width=True
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)
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# Tab 2: Model Performance
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with tab2:
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st.write("### Model Performance")
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st.write(f"Mean Squared Error (MSE): {mse:.2f}")
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st.write(f"R-squared: {r2:.2f}")
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# Tab 3: Prediction
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with tab3:
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st.write("### Predict Completion Time")
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project_size_input = st.number_input("Project Size (Lines of Code)", min_value=1000, value=10000, step=1000)
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num_developers_input = st.number_input("Number of Developers", min_value=1, value=5, step=1)
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complexity_input = st.slider("Complexity (1-5)", min_value=1, max_value=5, value=3, step=1)
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if st.button("Predict"):
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# Create input array for prediction
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input_data = [[project_size_input, num_developers_input, complexity_input]]
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# Make prediction
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prediction = model.predict(input_data)[0]
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st.write(f"### Predicted Completion Time: {prediction:.2f}")
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requirements.txt
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pandas
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numpy
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streamlit
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altair
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scikit-learn
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software_project_data.csv
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Project_Size,Num_Developers,Complexity,Completion_Time
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