Spaces:
Sleeping
Sleeping
File size: 2,196 Bytes
8f460b5 1840ab8 d481617 6dd2291 d481617 f4ba322 d481617 f4ba322 7a97b42 1840ab8 f4ba322 1840ab8 f4ba322 1840ab8 f4ba322 d481617 f4ba322 1840ab8 f4ba322 d481617 f4ba322 d481617 f4ba322 1840ab8 f4ba322 |
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 |
import streamlit as st
import pandas as pd
from sklearn.linear_model import LinearRegression
# Page config
st.set_page_config(page_title="Crime Rate Prediction", layout="wide")
st.title("📊 Crime Rate Prediction Based on Past Data")
# Replace this with your actual dataset path
csv_path = "https://huggingface.co/spaces/MLDeveloper/crime_rate_predicition/resolve/main/RS_Session_255_AS_116.1%20(2).csv" # Example: "data/crime_data.csv" if inside a folder
# Load data
try:
df = pd.read_csv(csv_path)
# Display raw data
st.subheader("📄 Raw Dataset")
st.dataframe(df)
# Extract the relevant columns
data = df[[
'State/UT',
'Number of Cases Registered - 2018-19',
'Number of Cases Registered - 2019-20',
'Number of Cases Registered - 2020-21',
'Number of Cases Registered - 2021-22 (up to 31.10.2021)'
]].copy()
# Rename for easier access
data.columns = ['State/UT', '2018', '2019', '2020', '2021']
# Model training & prediction
years = ['2018', '2019', '2020', '2021']
future_year = '2022'
X = pd.DataFrame({'Year': [2018, 2019, 2020, 2021]})
predicted_values = []
for i, row in data.iterrows():
y = row[years].values
model = LinearRegression()
model.fit(X, y)
pred = model.predict([[2022]])[0]
predicted_values.append(max(0, int(pred))) # Avoid negatives
data['Predicted 2022'] = predicted_values
# Display result
st.subheader("📈 Predicted Crime Rate for 2022")
st.dataframe(data[['State/UT', 'Predicted 2022']].sort_values(by='Predicted 2022', ascending=False))
# Plot top 10 states
st.subheader("🔝 Top 10 States by Predicted Crime Rate")
top10 = data.sort_values(by='Predicted 2022', ascending=False).head(10)
fig, ax = plt.subplots()
ax.barh(top10['State/UT'], top10['Predicted 2022'], color='salmon')
ax.set_xlabel("Predicted Cases")
ax.set_ylabel("State/UT")
ax.invert_yaxis()
ax.set_title("Top 10 States with Highest Predicted Crime Rate (2022)")
st.pyplot(fig)
except FileNotFoundError:
st.error(f"❌ File not found at path: {csv_path}. Please check the path.")
|