Spaces:
Sleeping
Sleeping
File size: 1,428 Bytes
e2e2eec | 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 | import sqlite3
import pandas as pd
from sklearn.preprocessing import MinMaxScaler
# Connect to database
conn = sqlite3.connect('sustainable_farming.db')
### === Farmer Data Cleaning === ###
farmer_df = pd.read_sql_query("SELECT * FROM farmer_advisor", conn)
farmer_scaler = MinMaxScaler()
farmer_columns_to_normalize = [
'Soil_pH', 'Soil_Moisture', 'Temperature_C', 'Rainfall_mm',
'Fertilizer_Usage_kg', 'Pesticide_Usage_kg', 'Crop_Yield_ton', 'Sustainability_Score'
]
farmer_df[farmer_columns_to_normalize] = farmer_scaler.fit_transform(farmer_df[farmer_columns_to_normalize])
farmer_df.to_sql('farmer_advisor_normalized', conn, if_exists='replace', index=False)
print("✅ Farmer data normalized and saved.")
### === Market Data Cleaning === ###
market_df = pd.read_sql_query("SELECT * FROM market_researcher", conn)
market_scaler = MinMaxScaler()
market_columns_to_normalize = [
'Market_Price_per_ton',
'Demand_Index',
'Supply_Index',
'Competitor_Price_per_ton',
'Economic_Indicator',
'Weather_Impact_Score',
'Consumer_Trend_Index'
]
market_df[market_columns_to_normalize] = market_scaler.fit_transform(market_df[market_columns_to_normalize])
# Keep 'Product' and 'Seasonal_Factor' as-is
market_df.to_sql('market_researcher_normalized', conn, if_exists='replace', index=False)
print("✅ Market data normalized and saved.")
conn.close()
|