College Dataset<\/strong><\/a><\/p>\n# To build a machine learning model that can predict the annual tuition fee of a\r\n# private college based on:\r\n# Its ranking , The student satisfaction score , The placement rate\r\n\r\nimport pandas as pd\r\nimport numpy as np\r\nfrom sklearn.linear_model import LinearRegression\r\nimport tkinter as tk\r\nfrom tkinter import messagebox\r\nimport seaborn as sns\r\nimport matplotlib.pyplot as plt\r\n# Load and train the model\r\ndf = pd.read_excel(\"D:\/\/MLFile\/college_data.xlsx\")\r\ndef train_model():\r\n\r\n X = df[['Ranking', 'Student_Satisfaction', 'Placement_Rate (%)']] # Independent variable\r\n y = df['Tuition_Fee ($)'] # depended variable\r\n model = LinearRegression()\r\n model.fit(X, y) # Train\r\n return model\r\n\r\nmodel = train_model()\r\n\r\n# GUI Function to predict tuition fee\r\n\r\ndef predict_fee():\r\n try:\r\n rank = float(entry_rank.get())\r\n satisfaction = float(entry_satisfaction.get())\r\n placement = float(entry_placement.get())\r\n\r\n input_data = np.array([[rank, satisfaction, placement]])\r\n predicted = model.predict(input_data)[0]\r\n\r\n result_label.config(text=f\"Predicted Tuition Fee: ${predicted:,.2f}\")\r\n\r\n # Plot: Compare predicted fee with average\r\n avg_fee = df['Tuition_Fee ($)'].mean()\r\n data = {\r\n 'Type': ['Predicted College', 'Average Fee'],\r\n 'Tuition Fee ($)': [predicted, avg_fee]\r\n }\r\n plot_df = pd.DataFrame(data)\r\n # print(plot_df)\r\n sns.barplot(x='Type', y='Tuition Fee ($)', data=plot_df, palette=\"Blues_d\")\r\n plt.title(\"Predicted vs Average Tuition Fee\")\r\n plt.ylabel(\"Fee in $\")\r\n plt.tight_layout()\r\n plt.show()\r\n\r\n except ValueError:\r\n messagebox.showerror(\"Invalid Input\", \"Please enter numeric values.\")\r\n\r\n\r\n\r\n# def predict_fee():\r\n# try:\r\n# rank = float(entry_rank.get())\r\n# satisfaction = float(entry_satisfaction.get())\r\n# placement = float(entry_placement.get())\r\n#\r\n# # Predict using the model\r\n# input_data = np.array([[rank, satisfaction, placement]])\r\n# fee = model.predict(input_data) # Predication\r\n#\r\n# result_label.config(text=f\"Predicted Tuition Fee: ${fee[0]:,.2f}\")\r\n# except ValueError:\r\n# messagebox.showerror(\"Invalid Input\", \"Please enter numeric values.\")\r\n\r\n# Create GUI window\r\nroot = tk.Tk()\r\nroot.title(\"Tuition Fee Predictor for Colleges\")\r\nroot.geometry(\"500x400\")\r\nroot.resizable(False, False)\r\n\r\n# Title label\r\ntk.Label(root, text=\"Tuition Fee Predictor\", font=(\"Helvetica\", 25, \"bold\")).pack(pady=10)\r\n# Ranking\r\ntk.Label(root, text=\"College Ranking (1 = Best):\",font=(\"Arial\", 12, \"bold\")).pack()\r\nentry_rank = tk.Entry(root,font=(\"Arial\", 12,\"bold\")) # TextBox\r\nentry_rank.pack()\r\n# Student Satisfaction\r\ntk.Label(root, text=\"Student Satisfaction Score (0 - 10):\",font=(\"Arial\", 12, \"bold\")).pack()\r\nentry_satisfaction = tk.Entry(root,font=(\"Arial\", 12,\"bold\")) # TextBox\r\nentry_satisfaction.pack()\r\n# Placement Rate\r\ntk.Label(root, text=\"Placement Rate (%):\",font=(\"Arial\", 12, \"bold\")).pack()\r\nentry_placement = tk.Entry(root,font=(\"Arial\", 12,\"bold\")) # TextBox\r\nentry_placement.pack()\r\n\r\n# Predict Button\r\ntk.Button(root, text=\"Predict Tuition Fee\", command=predict_fee, bg=\"blue\", fg=\"white\").pack(pady=10)\r\n\r\n# Result Label\r\nresult_label = tk.Label(root, text=\"\", font=(\"Arial\", 12, \"bold\"), fg=\"green\")\r\nresult_label.pack(pady=10)\r\n\r\n# Run the GUI loop\r\nroot.mainloop()<\/pre>\n <\/span><\/p>\n","protected":false},"excerpt":{"rendered":"Program 1 College Dataset # To build a machine learning model that can predict the annual tuition fee of a # private college based on: # Its ranking , The student satisfaction score ,...<\/p>\n","protected":false},"author":581,"featured_media":0,"comment_status":"open","ping_status":"closed","sticky":false,"template":"","format":"standard","meta":{"footnotes":""},"categories":[36],"tags":[8431,34745,33127,33128,20697,34749,34746,34748],"class_list":["post-145678","post","type-post","status-publish","format-standard","hentry","category-machine-learning","tag-machine-learning","tag-machine-learning-multiple-linear-regression-model-for-predicting-annual-tuition-fee","tag-machine-learning-practical","tag-machine-learning-program","tag-machine-learning-project","tag-multiple-linear-regression-model","tag-multiple-linear-regression-model-for-predicting-annual-tuition-fee","tag-predicting-annual-tuition-fee"],"yoast_head":"\n
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