saldoprediction.py

import csv
import pandas as pd
from sklearn.model_selection import train_test_split
from sklearn.linear_model import LinearRegression
from sklearn.metrics import mean_squared_error
import numpy as np
import calendar
from datetime import datetime

# Function to predict saldo for the last day of the current month
def predict_last_day_of_current_month(model):
    today = datetime.today()
    year = today.year
    month = today.month
    last_day = calendar.monthrange(year, month)[1]  # Get the last day of the current month
    date = pd.Timestamp(year, month, last_day)
    day_of_month = date.day
    day_of_week = date.dayofweek  # Monday=0, Sunday=6
    prediction = model.predict([[day_of_month, day_of_week]])
    return round(prediction[0], 2)

# Load your saldo data with a custom delimiter (,)
data = pd.read_csv('/home/PI/saldo_data.csv', delimiter=',')

# Remove duplicate entries
data = data.drop_duplicates(subset=['Date', 'Saldo'])

# Preprocess your data: Convert dates to features
data['Date'] = pd.to_datetime(data['Date'])
data['DayOfMonth'] = data['Date'].dt.day
data['DayOfWeek'] = data['Date'].dt.dayofweek  # Monday=0, Sunday=6

# Define your features and target variable
X = data[['DayOfMonth', 'DayOfWeek']]  # Using day of month and day of week as features
y = data['Saldo']

# Split the data into training and testing sets
X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=42)

# Define and train the model
model = LinearRegression()
model.fit(X_train, y_train)

# Make predictions and evaluate the model
predictions = model.predict(X_test)
print("RMSE:", np.sqrt(mean_squared_error(y_test, predictions)))

# Example usage of the prediction function for the last day of the current month
prediction = predict_last_day_of_current_month(model)
print("End of current month prediction:", prediction)