{"id":145699,"date":"2025-06-30T18:20:37","date_gmt":"2025-06-30T12:50:37","guid":{"rendered":"https:\/\/data-flair.training\/blogs\/?p=145699"},"modified":"2025-06-30T18:20:37","modified_gmt":"2025-06-30T12:50:37","slug":"college-admission-eligibility-predictor-using-decision-tree","status":"publish","type":"post","link":"https:\/\/data-flair.training\/blogs\/college-admission-eligibility-predictor-using-decision-tree\/","title":{"rendered":"ML Project &#8211; College Admission Eligibility Predictor using Decision Tree"},"content":{"rendered":"<h3>Program 1<\/h3>\n<p><a href=\"https:\/\/drive.google.com\/file\/d\/1-k-vTyyvlPR2k2ZoxmUv1jS64JxzDofJ\/view?usp=sharing\"><strong>Admission Dataset<\/strong><\/a><\/p>\n<pre class=\"EnlighterJSRAW\" data-enlighter-language=\"generic\"># College Admission Eligibility Predictor Using Decision Tree\r\n\r\nimport pandas as pd\r\nfrom sklearn.model_selection import train_test_split\r\nfrom sklearn.tree import DecisionTreeClassifier\r\nfrom sklearn.metrics import accuracy_score\r\n\r\n# Data Set Load\r\ndf=pd.read_csv('admission_data1.csv')\r\n# print(df.head())\r\n# print(df.isnull().sum())\r\n\r\n# Depended and Independed variables\r\nX = df[['GPA', 'EntranceExamScore', 'Extracurriculars', 'VolunteerHours']]\r\ny = df['Eligible']\r\n# Split Dataset\r\nX_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=1)\r\n# print(len(X_train))\r\n# print(len(y_train))\r\n# print(len(X_test))\r\n# print(len(y_test))\r\n\r\n# Model\r\nmodel = DecisionTreeClassifier()\r\nmodel.fit(X_train,y_train)\r\nprint(model)\r\n# Accurracy\r\ny_pred=model.predict(X_test)\r\nprint(\"Accuracy:\", accuracy_score(y_test, y_pred))\r\n\r\n# Predict new case\r\nsample = [[3.6, 87, 1, 10]]\r\nprediction = model.predict(sample)\r\nprint(\"\\nPrediction for new student:\", \"Eligible\" if prediction[0] == 1 else \"Not Eligible\")<\/pre>\n<h3>Program 2<\/h3>\n<pre class=\"EnlighterJSRAW\" data-enlighter-language=\"generic\">import tkinter as tk\r\nfrom tkinter import messagebox\r\nimport pandas as pd\r\nfrom sklearn.tree import DecisionTreeClassifier\r\n\r\n# Load and train\r\ndf = pd.read_csv(\"admission_data1.csv\")\r\nX = df[['GPA', 'EntranceExamScore', 'Extracurriculars', 'VolunteerHours']]\r\ny = df['Eligible']\r\nmodel = DecisionTreeClassifier()\r\nmodel.fit(X, y)\r\n\r\n# GUI App\r\ndef predict():\r\n    try:\r\n        gpa = float(entry_gpa.get())\r\n        score = int(entry_score.get())\r\n        extra = int(entry_extra.get())\r\n        hours = int(entry_hours.get())\r\n        result = model.predict([[gpa, score, extra, hours]])[0]\r\n        msg = \"Eligible for Admission\" if result == 1 else \"Not Eligible\"\r\n        messagebox.showinfo(\"Prediction\", msg)\r\n    except:\r\n        messagebox.showerror(\"Error\", \"Please enter valid input values.\")\r\n\r\napp = tk.Tk()\r\napp.title(\"College Admission Predictor\")\r\napp.geometry(\"350x300\")\r\n\r\ntk.Label(app, text=\"GPA (0.0 - 4.0)\").pack()\r\nentry_gpa = tk.Entry(app)\r\nentry_gpa.pack()\r\n\r\ntk.Label(app, text=\"Entrance Exam Score (0 - 100)\").pack()\r\nentry_score = tk.Entry(app)\r\nentry_score.pack()\r\n\r\ntk.Label(app, text=\"Extracurriculars (1=Yes, 0=No)\").pack()\r\nentry_extra = tk.Entry(app)\r\nentry_extra.pack()\r\n\r\ntk.Label(app, text=\"Volunteer Hours\").pack()\r\nentry_hours = tk.Entry(app)\r\nentry_hours.pack()\r\n\r\ntk.Button(app, text=\"Predict Eligibility\", command=predict).pack(pady=10)\r\n\r\napp.mainloop()<\/pre>\n<p>&nbsp;<\/p>\n<p>&nbsp;<span hidden class=\"__iawmlf-post-loop-links\" data-iawmlf-links=\"[{&quot;id&quot;:42,&quot;href&quot;:&quot;https:\\\/\\\/drive.google.com\\\/file\\\/d\\\/1-k-vTyyvlPR2k2ZoxmUv1jS64JxzDofJ\\\/view?usp=sharing&quot;,&quot;archived_href&quot;:&quot;http:\\\/\\\/web-wp.archive.org\\\/web\\\/20251205111016\\\/https:\\\/\\\/drive.google.com\\\/file\\\/d\\\/1-k-vTyyvlPR2k2ZoxmUv1jS64JxzDofJ\\\/view?usp=sharing&quot;,&quot;redirect_href&quot;:&quot;&quot;,&quot;checks&quot;:[{&quot;date&quot;:&quot;2025-12-06 00:17:50&quot;,&quot;http_code&quot;:200},{&quot;date&quot;:&quot;2026-01-03 11:59:05&quot;,&quot;http_code&quot;:200},{&quot;date&quot;:&quot;2026-01-10 13:02:12&quot;,&quot;http_code&quot;:200},{&quot;date&quot;:&quot;2026-03-02 14:24:29&quot;,&quot;http_code&quot;:200},{&quot;date&quot;:&quot;2026-03-06 02:15:27&quot;,&quot;http_code&quot;:200},{&quot;date&quot;:&quot;2026-03-29 05:49:41&quot;,&quot;http_code&quot;:200},{&quot;date&quot;:&quot;2026-04-18 22:31:40&quot;,&quot;http_code&quot;:200},{&quot;date&quot;:&quot;2026-04-22 06:55:31&quot;,&quot;http_code&quot;:200},{&quot;date&quot;:&quot;2026-04-29 04:46:37&quot;,&quot;http_code&quot;:200},{&quot;date&quot;:&quot;2026-05-05 22:01:26&quot;,&quot;http_code&quot;:200},{&quot;date&quot;:&quot;2026-05-27 20:02:02&quot;,&quot;http_code&quot;:200},{&quot;date&quot;:&quot;2026-06-01 08:40:00&quot;,&quot;http_code&quot;:200}],&quot;broken&quot;:false,&quot;last_checked&quot;:{&quot;date&quot;:&quot;2026-06-01 08:40:00&quot;,&quot;http_code&quot;:200},&quot;process&quot;:&quot;done&quot;}]\"><\/span><\/p>\n","protected":false},"excerpt":{"rendered":"<p>Program 1 Admission Dataset # College Admission Eligibility Predictor Using Decision Tree import pandas as pd from sklearn.model_selection import train_test_split from sklearn.tree import DecisionTreeClassifier from sklearn.metrics import accuracy_score # Data Set Load df=pd.read_csv(&#8216;admission_data1.csv&#8217;) #&#46;&#46;&#46;<\/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":[34760,34759,3626,8431,33127,33128,20697],"class_list":["post-145699","post","type-post","status-publish","format-standard","hentry","category-machine-learning","tag-college-admission-eligibility-predictor","tag-college-admission-eligibility-predictor-using-decision-tree","tag-decision-tree","tag-machine-learning","tag-machine-learning-practical","tag-machine-learning-program","tag-machine-learning-project"],"yoast_head":"<!-- This site is optimized with the Yoast SEO plugin v27.8 - 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