Tools: 💰 Build a Salary Prediction System in Python with a GUI

In this tutorial, we’ll build SalaryPredictor v2.0, a desktop app that predicts employee salaries using Machine Learning. It supports batch predictions, real-time previews, filtering, and live search—all in a beautiful GUI with tkinter and ttkbootstrap. We’ll break it down step by step so even beginners can follow along. Step 1: Install Required Packages Before we start, make sure you have Python installed. Then install the dependencies: pip install pandas joblib tkinterdnd2 ttkbootstrap pandas: For handling CSV files joblib: To load ML models tkinter + ttkbootstrap: For GUI tkinterdnd2: Optional, for drag-and-drop support Step 2: Set Up the Imports Create a Python file called salary_predictor.py and start with the imports: Optional drag-and-drop support: ✅ Explanation:
We import packages for GUI, CSV handling, threading, and machine learning. tkinterdnd2 enables drag-and-drop of files for convenience. Step 3: Helper Function for Resource Paths If you package your app using PyInstaller, you need a helper to locate files: Helps the app locate icons or other files when bundled. Step 4: Create the Salary Prediction Worker This class handles predictions in a background thread, so the GUI doesn’t freeze: The main prediction loop: Uses only the model’s features (Age, Experience, EducationLevel). Predicts salary for each employee. Sends results back to GUI via callback functions. Step 5: Build the GUI App Sets up main variables and GUI window. Step 6: File Selection and Drag-and-Drop Allow users to select files or folders: Drag-and-drop (if tkinterdnd2 is installed): Step 7: Display Employee Predictions in a Table Use ttk.Treeview to show real-time results: ✅ Explanation:
Each row shows: Checkbox for selecting rows Step 8: Start Prediction Start worker in a background thread: Step 9: Filter and Search Filter by min/max salary and search by employee name: ✅ Explanation:
Users can filter the table dynamically as data is processed. Step 10: Export Results Export selected rows to a CSV: Finally, start the GUI loop: Step 12: Clone the Full Project You can find the full source code and download it here:
👉 https://github.com/rogers-cyber/python-tiny-tools/tree/main/Salary-prediction-system Drag & drop CSV files or folders Real-time per-employee salary prediction Filter by min/max salary Search employees live Batch processing with progress bar Export results to CSV This tutorial is designed to be beginner-friendly and easy to follow. Templates let you quickly answer FAQs or store snippets for re-use. Are you sure you want to hide this comment? It will become hidden in your post, but will still be visible via the comment's permalink. Hide child comments as well For further actions, you may consider blocking this person and/or reporting abuse CODE_BLOCK:
import os
import sys
import threading
import pandas as pd
import joblib
import tkinter as tk
from tkinter import filedialog, messagebox, ttk
import ttkbootstrap as tb
from ttkbootstrap.constants import * Enter fullscreen mode Exit fullscreen mode CODE_BLOCK:
import os
import sys
import threading
import pandas as pd
import joblib
import tkinter as tk
from tkinter import filedialog, messagebox, ttk
import ttkbootstrap as tb
from ttkbootstrap.constants import * CODE_BLOCK:
import os
import sys
import threading
import pandas as pd
import joblib
import tkinter as tk
from tkinter import filedialog, messagebox, ttk
import ttkbootstrap as tb
from ttkbootstrap.constants import * CODE_BLOCK:
try: from tkinterdnd2 import TkinterDnD, DND_FILES DND_ENABLED = True
except ImportError: DND_ENABLED = False print("Drag & Drop requires tkinterdnd2: pip install tkinterdnd2") Enter fullscreen mode Exit fullscreen mode CODE_BLOCK:
try: from tkinterdnd2 import TkinterDnD, DND_FILES DND_ENABLED = True
except ImportError: DND_ENABLED = False print("Drag & Drop requires tkinterdnd2: pip install tkinterdnd2") CODE_BLOCK:
try: from tkinterdnd2 import TkinterDnD, DND_FILES DND_ENABLED = True
except ImportError: DND_ENABLED = False print("Drag & Drop requires tkinterdnd2: pip install tkinterdnd2") CODE_BLOCK:
def resource_path(file_name): base_path = getattr(sys, "_MEIPASS", os.path.dirname(os.path.abspath(__file__))) return os.path.join(base_path, file_name) Enter fullscreen mode Exit fullscreen mode CODE_BLOCK:
def resource_path(file_name): base_path = getattr(sys, "_MEIPASS", os.path.dirname(os.path.abspath(__file__))) return os.path.join(base_path, file_name) CODE_BLOCK:
def resource_path(file_name): base_path = getattr(sys, "_MEIPASS", os.path.dirname(os.path.abspath(__file__))) return os.path.join(base_path, file_name) CODE_BLOCK:
class SalaryPredictWorker: def __init__(self, files, model_path, callbacks): self.files = files self.model = joblib.load(model_path) self.callbacks = callbacks self._running = True def stop(self): self._running = False Enter fullscreen mode Exit fullscreen mode CODE_BLOCK:
class SalaryPredictWorker: def __init__(self, files, model_path, callbacks): self.files = files self.model = joblib.load(model_path) self.callbacks = callbacks self._running = True def stop(self): self._running = False CODE_BLOCK:
class SalaryPredictWorker: def __init__(self, files, model_path, callbacks): self.files = files self.model = joblib.load(model_path) self.callbacks = callbacks self._running = True def stop(self): self._running = False CODE_BLOCK:
def run(self): feature_cols = ["Age", "Experience", "EducationLevel"] for path in self.files: if not self._running: break df = pd.read_csv(path) X = df[feature_cols] df['PredictedSalary'] = self.model.predict(X) for idx, row in df.iterrows(): if not self._running: break row_data = row.to_dict() row_data['_row_id'] = idx if "found" in self.callbacks: self.callbacks["found"](path, row_data) Enter fullscreen mode Exit fullscreen mode CODE_BLOCK:
def run(self): feature_cols = ["Age", "Experience", "EducationLevel"] for path in self.files: if not self._running: break df = pd.read_csv(path) X = df[feature_cols] df['PredictedSalary'] = self.model.predict(X) for idx, row in df.iterrows(): if not self._running: break row_data = row.to_dict() row_data['_row_id'] = idx if "found" in self.callbacks: self.callbacks["found"](path, row_data) CODE_BLOCK:
def run(self): feature_cols = ["Age", "Experience", "EducationLevel"] for path in self.files: if not self._running: break df = pd.read_csv(path) X = df[feature_cols] df['PredictedSalary'] = self.model.predict(X) for idx, row in df.iterrows(): if not self._running: break row_data = row.to_dict() row_data['_row_id'] = idx if "found" in self.callbacks: self.callbacks["found"](path, row_data) COMMAND_BLOCK:
class SalaryPredictApp: APP_NAME = "SalaryPredictor" APP_VERSION = "2.0" def __init__(self): self.root = tb.Window(themename="darkly") # Use ttkbootstrap for a nice theme self.root.title(f"{self.APP_NAME} v{self.APP_VERSION}") self.root.minsize(1300, 750) self.file_set = set() self.model_path = tk.StringVar() self.filter_min = tk.DoubleVar(value=0) self.filter_max = tk.DoubleVar(value=1e9) self.search_var = tk.StringVar() Enter fullscreen mode Exit fullscreen mode COMMAND_BLOCK:
class SalaryPredictApp: APP_NAME = "SalaryPredictor" APP_VERSION = "2.0" def __init__(self): self.root = tb.Window(themename="darkly") # Use ttkbootstrap for a nice theme self.root.title(f"{self.APP_NAME} v{self.APP_VERSION}") self.root.minsize(1300, 750) self.file_set = set() self.model_path = tk.StringVar() self.filter_min = tk.DoubleVar(value=0) self.filter_max = tk.DoubleVar(value=1e9) self.search_var = tk.StringVar() COMMAND_BLOCK:
class SalaryPredictApp: APP_NAME = "SalaryPredictor" APP_VERSION = "2.0" def __init__(self): self.root = tb.Window(themename="darkly") # Use ttkbootstrap for a nice theme self.root.title(f"{self.APP_NAME} v{self.APP_VERSION}") self.root.minsize(1300, 750) self.file_set = set() self.model_path = tk.StringVar() self.filter_min = tk.DoubleVar(value=0) self.filter_max = tk.DoubleVar(value=1e9) self.search_var = tk.StringVar() CODE_BLOCK:
def browse(self): folder = filedialog.askdirectory(title="Select Folder with CSV files") if folder: self._scan_and_queue_files_thread([folder]) Enter fullscreen mode Exit fullscreen mode CODE_BLOCK:
def browse(self): folder = filedialog.askdirectory(title="Select Folder with CSV files") if folder: self._scan_and_queue_files_thread([folder]) CODE_BLOCK:
def browse(self): folder = filedialog.askdirectory(title="Select Folder with CSV files") if folder: self._scan_and_queue_files_thread([folder]) CODE_BLOCK:
def on_drop(self, event): dropped_paths = self.root.tk.splitlist(event.data) self._scan_and_queue_files_thread(dropped_paths) Enter fullscreen mode Exit fullscreen mode CODE_BLOCK:
def on_drop(self, event): dropped_paths = self.root.tk.splitlist(event.data) self._scan_and_queue_files_thread(dropped_paths) CODE_BLOCK:
def on_drop(self, event): dropped_paths = self.root.tk.splitlist(event.data) self._scan_and_queue_files_thread(dropped_paths) CODE_BLOCK:
self.tree = ttk.Treeview(main, columns=("selected", "filename", "employee_name", "predicted_salary"), show="headings")
self.tree.heading("selected", text="✅")
self.tree.heading("filename", text="Filename")
self.tree.heading("employee_name", text="Employee")
self.tree.heading("predicted_salary", text="Predicted Salary")
self.tree.pack(fill="both", expand=True) Enter fullscreen mode Exit fullscreen mode CODE_BLOCK:
self.tree = ttk.Treeview(main, columns=("selected", "filename", "employee_name", "predicted_salary"), show="headings")
self.tree.heading("selected", text="✅")
self.tree.heading("filename", text="Filename")
self.tree.heading("employee_name", text="Employee")
self.tree.heading("predicted_salary", text="Predicted Salary")
self.tree.pack(fill="both", expand=True) CODE_BLOCK:
self.tree = ttk.Treeview(main, columns=("selected", "filename", "employee_name", "predicted_salary"), show="headings")
self.tree.heading("selected", text="✅")
self.tree.heading("filename", text="Filename")
self.tree.heading("employee_name", text="Employee")
self.tree.heading("predicted_salary", text="Predicted Salary")
self.tree.pack(fill="both", expand=True) CODE_BLOCK:
def start(self): selected_files = [self.tree.item(i)['values'][1] for i in self.tree.get_children()] self.worker_obj = SalaryPredictWorker(selected_files, self.model_path.get(), callbacks={ "found": self.add_result, "progress": self.set_target, "finished": self.finish }) threading.Thread(target=self.worker_obj.run, daemon=True).start() Enter fullscreen mode Exit fullscreen mode CODE_BLOCK:
def start(self): selected_files = [self.tree.item(i)['values'][1] for i in self.tree.get_children()] self.worker_obj = SalaryPredictWorker(selected_files, self.model_path.get(), callbacks={ "found": self.add_result, "progress": self.set_target, "finished": self.finish }) threading.Thread(target=self.worker_obj.run, daemon=True).start() CODE_BLOCK:
def start(self): selected_files = [self.tree.item(i)['values'][1] for i in self.tree.get_children()] self.worker_obj = SalaryPredictWorker(selected_files, self.model_path.get(), callbacks={ "found": self.add_result, "progress": self.set_target, "finished": self.finish }) threading.Thread(target=self.worker_obj.run, daemon=True).start() CODE_BLOCK:
def apply_filters(self): min_salary = self.filter_min.get() max_salary = self.filter_max.get() search_text = self.search_var.get().lower() self.tree.delete(*self.tree.get_children()) for key, row in self.all_rows.items(): if min_salary <= row['predicted_salary'] <= max_salary: if search_text in row['employee_name'].lower(): self.tree.insert("", "end", iid=key, values=(row['selected'], row['filename'], row['employee_name'], f"${row['predicted_salary']:,.2f}")) Enter fullscreen mode Exit fullscreen mode CODE_BLOCK:
def apply_filters(self): min_salary = self.filter_min.get() max_salary = self.filter_max.get() search_text = self.search_var.get().lower() self.tree.delete(*self.tree.get_children()) for key, row in self.all_rows.items(): if min_salary <= row['predicted_salary'] <= max_salary: if search_text in row['employee_name'].lower(): self.tree.insert("", "end", iid=key, values=(row['selected'], row['filename'], row['employee_name'], f"${row['predicted_salary']:,.2f}")) CODE_BLOCK:
def apply_filters(self): min_salary = self.filter_min.get() max_salary = self.filter_max.get() search_text = self.search_var.get().lower() self.tree.delete(*self.tree.get_children()) for key, row in self.all_rows.items(): if min_salary <= row['predicted_salary'] <= max_salary: if search_text in row['employee_name'].lower(): self.tree.insert("", "end", iid=key, values=(row['selected'], row['filename'], row['employee_name'], f"${row['predicted_salary']:,.2f}")) CODE_BLOCK:
def export_results(self): path = filedialog.asksaveasfilename(defaultextension=".csv") if path: with open(path, "w", encoding="utf-8") as f: f.write("Filename,Employee,PredictedSalary\n") for i in self.tree.get_children(): values = self.tree.item(i)['values'] f.write(f"{values[1]},{values[2]},{values[3]}\n") Enter fullscreen mode Exit fullscreen mode CODE_BLOCK:
def export_results(self): path = filedialog.asksaveasfilename(defaultextension=".csv") if path: with open(path, "w", encoding="utf-8") as f: f.write("Filename,Employee,PredictedSalary\n") for i in self.tree.get_children(): values = self.tree.item(i)['values'] f.write(f"{values[1]},{values[2]},{values[3]}\n") CODE_BLOCK:
def export_results(self): path = filedialog.asksaveasfilename(defaultextension=".csv") if path: with open(path, "w", encoding="utf-8") as f: f.write("Filename,Employee,PredictedSalary\n") for i in self.tree.get_children(): values = self.tree.item(i)['values'] f.write(f"{values[1]},{values[2]},{values[3]}\n") CODE_BLOCK:
if __name__ == "__main__": app = SalaryPredictApp() app.run() Enter fullscreen mode Exit fullscreen mode CODE_BLOCK:
if __name__ == "__main__": app = SalaryPredictApp() app.run() CODE_BLOCK:
if __name__ == "__main__": app = SalaryPredictApp() app.run()