#OVERALL PREDICTION OF BUDGET from tkinter import * import pandas as pd import numpy as np from sklearn.linear_model import LinearRegression from sklearn.model_selection import train_test_split from sklearn.metrics import mean_squared_error import csv      from tkinter import filedialog root=Tk() root.geometry('1370x700+0+0')  root.title('NMRL FBE') header=Frame(root,width=1370,bd=4) header.pack(side=TOP,fill=X)  def submit_path(): #path=e1.get() path = file_path year=int(e2.get())  # Load data from CSV df = pd.read_csv(path) 

Extract years and department names

years = df['Year']
departments = df.columns[1:]  # Exclude the 'Year' column

# Convert to numpy array for modeling
X = np.array(years).reshape(-1, 1)  # Years as input feature

# Create a dictionary to store department-wise forecasted amounts
dept_forecast = {}

# Loop through departments to train individual models (if needed) and store predictions
for dept in departments:
    y = df[dept]  # Forecasted amounts for current department
    
    # Train a linear regression model
    model = LinearRegression()
    model.fit(X, y)
    
    # Store the model for this department
    dept_forecast[dept] = model

# Function to predict total forecasted amount for any given year
def predict_total_forecast(year):
    total_forecast = 0
    for dept, model in dept_forecast.items():
        # Predict forecasted amount for the given year using the model for each department
        forecast_amount = model.predict(np.array(year).reshape(-1, 1))[0]
        total_forecast += forecast_amount
    return total_forecast 
 











# Example: Predict total forecast for year 2025
#a=int(input("Enter your required year:"))
year_to_predict = year
total_forecast_2025 = predict_total_forecast(year_to_predict)

result_text=f'Total forecasted amount for the organization in {year_to_predict}: {total_forecast_2025}'
output=Frame(root,width=1370,bd=6,bg='black')
output.pack(side=TOP)
Label(output,text=result_text,font=('Times New Roman',14,'bold'),fg='aqua',bg='black',bd=6,pady=10).pack(side=TOP)
Button(output,text='Clear Result',width=15,bd=3,command=output.destroy).pack(side=TOP)
#root.destroy()
 

#def submit_year(): #year=e2.get() #return year

#div=e3.get()

def read_csv_file(file_path): with open(file_path, 'r') as file: reader = csv.reader(file) content = [row for row in reader] return content  def browse_files(): global file_path file_path = filedialog.askopenfilename(filetypes=[("CSV files", "*.csv")]) if file_path: content = read_csv_file(file_path) print(file_path) #for row in content: # print(row)  Label(header,text='NMRL FBE',font=('Times New Roman',36,'bold'),fg='black',bg='orange',bd=6,relief='ridge').pack(side=TOP,fill=X) body=Frame(root,width=1370,bd=6,bg='yellowgreen',relief='ridge') body.pack(side=TOP,fill=X)    Label(body,text='Select dataset to predict:',font=('Times New Roman',14,'bold'),bg='yellowgreen',fg='black').pack(side=LEFT) #e1=Entry(body,width=20, font = ('Times New Roman',14,'normal'),bd=4) #e1.pack(side=LEFT,padx=20) browse_button = Button(body, text="Browse Documents", command=browse_files) browse_button.pack(side= LEFT)  Label(body,text='Enter required year:',font=('Times New Roman',14,'bold'),bg='yellowgreen',fg='black').pack(side=LEFT) e2=Entry(body, font = ('Times New Roman',14,'normal'),bd=4) e2.pack(side=LEFT,padx=20)  #Label(body,text='Enter required division:',font=('Times New Roman',14,'bold'),bg='yellowgreen',fg='black').pack(side=LEFT) #e2=Entry(body, font = ('Times New Roman',14,'normal'),bd=4) #e2.pack(side=LEFT,padx=20)  btn_frame=Frame(root,width=1370,bd=4) btn_frame.pack(side=TOP)  sbt_btn=Button(btn_frame,text='Submit',width=15,bd=3,command=submit_path) sbt_btn.pack(side=LEFT)  #sbt_btn=Button(btn_frame,text='Run Model',width=15,bd=3,command=submit_year) #sbt_btn.pack(side=LEFT) 

mainloop()