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Combating Fraudulent Transactions: A Deep Dive into Credit Card Fraud Detection
- 3. What is Credit Card fraud ?? E X A M P L E S Insider Fraud Phishing Skimming Identity Theft Credit card fraud is unauthorized use of someone's card for purchases, causing financial loss and inconvenience. “To Predict whether the transactions are fraudulent or not”
- 4. Data Acquisition & Description Data preprocessing Exploratory Data Analysis Data Preparation Model Selection and Model Training Conclusion Work Flow
- 5. 🤷♂️ No missing values. Credit Card Fraud Detection Dataset 2023 Dataset Column s 31 Rows 568630 Features (Columns) id : Unique identifier for each transaction V1-V28: Anonymized features representing various transaction attributes (e.g., time, location, etc.) Amount : The transaction amount Class: Binary label indicating whether the transaction is fraudulent(1) or not (0) 🤷♂️ No duplicates. 👍 Data type also looks fine Data preprocessing
- 6. A well-structured dataset with 568,630 rows and 31 columns, featuring no null values and balanced distribution, provides a reliable foundation for in-depth analyses, yielding valuable insights across various domains Enhanced security, reduced financial losses, and improved customer trust through identification of fraudulent credit card transactions”
- 7. EDA Exploratory Data Analysis Exploratory Data Analysis (EDA) is a statistical approach to analyze and visualize data sets, helping to discover patterns, relationships, and insights for better understanding and decision-making df.info() df.info() provides concise information about a Data Frame, including data types, non-null count and memory usage. df.describe () Summarizes Data Frame statistics like mean, standard deviation, and quartiles, offering insights into numerical data distribution and central tendencies. df.shape Displays the number of rows and columns in Data Frame. Df.dtype() Shows data types of each column in Data Frame.
- 8. 1:- V17 and V18 are highly co-related. 2:- V16 and V17 are highly co-related. 3:- V9 and V10 are also positively co-related. 4:- V14 has a negative correlation with V4. A heatmap visually represents data intensity using color variations, with warmer colors indicating higher values and cooler colors indicating lower values. #Lets look data at heatmap paper = plt.figure(figsize=[2 0,12]) sns.heatmap(df.cor r(),cmap='BuPu',an not=True) plt.title('Correlation Heatmap',color='re d') plt.show()
- 9. df.skew() id -6.579536e-16 V1 -8.341717e-02 V2 - 1.397952e+00 V3 1.462221e-02 V4 - 4.416893e-02 V5 1.506414e+00 V6 - 2.016110e-01 V7 1.902687e+01 V8 2.999722e-01 V9 1.710575e-01 V10 7.404136e-01 V11 - 2.089056e-02 V12 6.675895e-02 V13 1.490639e-02 V14 2.078348e-01 V15 1.123298e-02 V16 2.664070e-01 V17 3.730610e-01 V18 1.291911e-01 V19 - 1.017123e-02 V20 - 1.556460e+00 V21 - 1.089833e-01 V22 3.185295e-01 V23 - 9.968746e-02 V24 6.608974e-02 V25 2.300804e-02 V26 - 1.895874e-02 V27 2.755452e+00 V28 1.724978e+00 Amount 1.655585e-03 Class 0.000000e+00 dtype: float64 Observations (●'◡’●):-- Features like V1,V23 are highly negatively skewed. plt.figure(figsize=(6, 4)) # Adjust the figure size as needed sns.countplot(x='Class', data=df) plt.xlabel('Class') plt.ylabel('Count') plt.title('Distribution of Class') plt.show()
- 10. df['Amount'].plot.box() A box plot, or box-and-whisker plot, displays the distribution of a dataset, showing the median, quartiles, and outliers. It provides a visual summary of central tendency and spread.
- 11. # Assuming 'df' is DataFrame and 'Amount' is a column in it sns.kdeplot(data=df['Amount'], fill=True) plt.show() A KDE (Kernel Density Estimate) plot depicts the probability density function of a continuous variable, smoothing data distribution visually. Observations : ♊ Amount is fairly Normally distributed.
- 12. # Lets plot a histogram paper, axes = plt.subplots(2, 2, figsize=(10, 6)) df['V1'].plot(kind='hist', ax=axes[0,0], title='Distribution of V1') df['V10'].plot(kind='hist', ax=axes[0,1], title='Distribution of V10') df['V12'].plot(kind='hist', ax=axes[1,0], title='Distribution of V12') df['V23'].plot(kind='hist', ax=axes[1,1], title='Distribution of V23') plt.suptitle('Distribution of V1,V10,V12 and V23',size=14) plt.tight_layout() plt.show()
- 13. Data Preparation Dividing Dataset into “X” and “Y” Shape of X (568630, 29) Shape of Y (568630) Let's standardize all our features to bring them on the same scale. #I have used standard scaler Feature Scaling sc = StandardScaler() x_scaled = sc.fit_transform(x) x_scaled_df = pd.DataFrame(x_scaled,columns=x.c olumns)
- 14. Model Selection and Model Training Dividing dataset into Training Data and Testing Data # Lets Split our dataset into train and test x_train,x_test,y_train,y_test = train_test_split(x_scaled_df,y,test_size=0.25,random_state=15,stratify= y) Decision Tree Model Model Classification report Accuracy Score:- 99.80022228787687
- 15. Random Forest Classifier Model Classification report Accuracy Score:- 96.44480085538626 Logistic regression Model Classification report Accuracy Score:- 99.98454426173694
- 16. ALGORITHM ACCURA CY CONFUSION MATRIX CLASSIFICATTION REPORT Decision Tree Model 99.800222 % Random Forest Classifier 99.984544 2% Logistic Regression 96.444800 8%
- 17. Conclusion •We have done Exploratory Data analysis for different features. •We prepared our Data and built different ML Models. •We have seen two different models, and how they are performing w.r.t Accuracy, Precision. • The Decision Tree Method has a higher accuracy score on the test dataset. •We have created the confusion matrix to see the details of the prediction accuracy of each model.
- 18. Thank YOU