{"id":145936,"date":"2025-07-16T16:14:22","date_gmt":"2025-07-16T10:44:22","guid":{"rendered":"https:\/\/data-flair.training\/blogs\/?p=145936"},"modified":"2025-07-16T16:14:22","modified_gmt":"2025-07-16T10:44:22","slug":"credit-card-fraud-detection-using-random-forest","status":"publish","type":"post","link":"https:\/\/data-flair.training\/blogs\/credit-card-fraud-detection-using-random-forest\/","title":{"rendered":"ML Project &#8211; Credit Card Fraud Detection using Random Forest"},"content":{"rendered":"<h3>Program 1<\/h3>\n<p><a href=\"https:\/\/drive.google.com\/file\/d\/1Q84Lf3w4tZQYt9vmbV-jwYDSvZB65kAp\/view?usp=sharing\" target=\"_blank\" rel=\"noopener\"><strong>Credit Card Fraud Dataset<\/strong><\/a><\/p>\n<pre class=\"EnlighterJSRAW\" data-enlighter-language=\"generic\">import pandas as pd\r\nimport numpy as np\r\nfrom tkinter import *\r\nfrom sklearn.ensemble import RandomForestClassifier\r\nfrom sklearn.model_selection import train_test_split\r\nimport matplotlib.pyplot as plt\r\nimport seaborn as sns\r\n\r\n# Load dataset\r\ndf = pd.read_csv(\"D:\/\/scikit_data\/card\/credit_card_fraud_large.csv\")\r\n\r\ndf.head()\r\n\r\ndf.isnull().sum()\r\n\r\nX = df.drop(\"IsFraud\", axis=1) # Independed variables\r\ny = df[\"IsFraud\"] # Depended variables\r\n\r\nX\r\n\r\ny\r\n\r\n# Train model\r\nmodel = RandomForestClassifier(n_estimators=100, random_state=42)\r\nmodel.fit(X, y)\r\n\r\n# GUI setup\r\nroot = Tk()\r\nroot.title(\"Credit Card Fraud Predictor\")\r\nroot.geometry(\"400x400\")\r\n\r\n# Input fields\r\nLabel(root, text=\"Transaction Amount\",font=(\"Arial\", 10, \"bold\") ).pack()\r\namt_entry = Entry(root)\r\namt_entry.pack()\r\n\r\nLabel(root, text=\"Transaction Time\",font=(\"Arial\", 10, \"bold\")).pack()\r\ntime_entry = Entry(root)\r\ntime_entry.pack()\r\n\r\nLabel(root, text=\"Location Risk (0=Safe, 1=Risky)\",font=(\"Arial\", 10, \"bold\")).pack()\r\nloc_entry = Entry(root)\r\nloc_entry.pack()\r\n\r\nLabel(root, text=\"Card Type (0=Debit, 1=Credit)\",font=(\"Arial\", 10, \"bold\")).pack()\r\ncard_entry = Entry(root)\r\ncard_entry.pack()\r\n\r\nresult_label = Label(root, text=\"\", font=(\"Arial\", 12, \"bold\"))\r\nresult_label.pack(pady=10)\r\n\r\ndef predict_fraud():\r\n    amt = float(amt_entry.get())\r\n    time = float(time_entry.get())\r\n    loc = int(loc_entry.get())\r\n    card = int(card_entry.get())\r\n\r\n    pred = model.predict([[amt, time, loc, card]])\r\n    result = \" Fraudulent Transaction!\" if pred[0] == 1 else \" Transaction Safe.\"\r\n    result_label.config(text=result)\r\n\r\ndef show_feature_importance():\r\n    importance = model.feature_importances_\r\n    features = X.columns\r\n    sns.barplot(x=importance, y=features, palette=\"Set2\")\r\n    plt.title(\"Feature Importance in Fraud Detection\")\r\n    plt.xlabel(\"Importance Score\")\r\n    plt.ylabel(\"Feature\")\r\n    plt.tight_layout()\r\n    plt.show()\r\n\r\nButton(root, text=\"Predict\", command=predict_fraud, bg=\"green\", fg=\"white\").pack(pady=10)\r\nButton(root, text=\"Show Feature Importance\", command=show_feature_importance, bg=\"blue\", fg=\"white\").pack()\r\n\r\nroot.mainloop()\r\n<\/pre>\n<p><span hidden class=\"__iawmlf-post-loop-links\" data-iawmlf-links=\"[{&quot;id&quot;:32,&quot;href&quot;:&quot;https:\\\/\\\/drive.google.com\\\/file\\\/d\\\/1Q84Lf3w4tZQYt9vmbV-jwYDSvZB65kAp\\\/view?usp=sharing&quot;,&quot;archived_href&quot;:&quot;http:\\\/\\\/web-wp.archive.org\\\/web\\\/20251205110208\\\/https:\\\/\\\/drive.google.com\\\/file\\\/d\\\/1Q84Lf3w4tZQYt9vmbV-jwYDSvZB65kAp\\\/view?usp=sharing&quot;,&quot;redirect_href&quot;:&quot;&quot;,&quot;checks&quot;:[{&quot;date&quot;:&quot;2025-12-12 13:39:47&quot;,&quot;http_code&quot;:200},{&quot;date&quot;:&quot;2025-12-17 06:03:38&quot;,&quot;http_code&quot;:200},{&quot;date&quot;:&quot;2026-01-12 15:26:31&quot;,&quot;http_code&quot;:200},{&quot;date&quot;:&quot;2026-02-10 19:25:29&quot;,&quot;http_code&quot;:200},{&quot;date&quot;:&quot;2026-02-23 00:43:14&quot;,&quot;http_code&quot;:200},{&quot;date&quot;:&quot;2026-03-27 11:51:53&quot;,&quot;http_code&quot;:200},{&quot;date&quot;:&quot;2026-04-16 09:08:25&quot;,&quot;http_code&quot;:200},{&quot;date&quot;:&quot;2026-04-26 09:09:43&quot;,&quot;http_code&quot;:200},{&quot;date&quot;:&quot;2026-04-30 04:07:27&quot;,&quot;http_code&quot;:200},{&quot;date&quot;:&quot;2026-05-21 04:42:15&quot;,&quot;http_code&quot;:200},{&quot;date&quot;:&quot;2026-06-20 03:00:26&quot;,&quot;http_code&quot;:200},{&quot;date&quot;:&quot;2026-06-23 06:53:10&quot;,&quot;http_code&quot;:200}],&quot;broken&quot;:false,&quot;last_checked&quot;:{&quot;date&quot;:&quot;2026-06-23 06:53:10&quot;,&quot;http_code&quot;:200},&quot;process&quot;:&quot;done&quot;}]\"><\/span><\/p>\n","protected":false},"excerpt":{"rendered":"<p>Program 1 Credit Card Fraud Dataset import pandas as pd import numpy as np from tkinter import * from sklearn.ensemble import RandomForestClassifier from sklearn.model_selection import train_test_split import matplotlib.pyplot as plt import seaborn as sns&#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":[20622,34915,34913,34914,8431,34916,33127,33128,20697],"class_list":["post-145936","post","type-post","status-publish","format-standard","hentry","category-machine-learning","tag-credit-card-fraud-detection","tag-credit-card-fraud-detection-in-machine-learning","tag-credit-card-fraud-detection-project","tag-credit-card-fraud-detection-using-random-forest","tag-machine-learning","tag-machine-learning-credit-card-fraud-detection-project","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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