diff --git a/explainableai/core.py b/explainableai/core.py
index 50fd1ed..5a903ea 100644
--- a/explainableai/core.py
+++ b/explainableai/core.py
@@ -1,4 +1,8 @@
-# explainableai/core.py
+import colorama
+from colorama import Fore, Style
+
+# Initialize colorama
+colorama.init(autoreset=True)
 
 import pandas as pd
 import numpy as np
@@ -21,7 +25,6 @@
 from reportlab.platypus import PageBreak
 
 
-
 class XAIWrapper:
     def __init__(self):
         self.model = None
@@ -47,12 +50,12 @@ def fit(self, models, X, y, feature_names=None):
         self.feature_names = feature_names if feature_names is not None else X.columns.tolist()
         self.is_classifier = all(hasattr(model, "predict_proba") for model in self.models.values())
 
-        print("Preprocessing data...")
+        print(f"{Fore.BLUE}Preprocessing data...{Style.RESET_ALL}")
         self._preprocess_data()
 
-        print("Fitting models and analyzing...")
+        print(f"{Fore.BLUE}Fitting models and analyzing...{Style.RESET_ALL}")
         self.model_comparison_results = self._compare_models()
-        
+
         # Select the best model based on cv_score
         best_model_name = max(self.model_comparison_results, key=lambda x: self.model_comparison_results[x]['cv_score'])
         self.model = self.models[best_model_name]
@@ -266,44 +269,44 @@ def _print_results(self, results):
 
     @staticmethod
     def perform_eda(df):
-        print("\nExploratory Data Analysis:")
-        print(f"Dataset shape: {df.shape}")
-        print("\nDataset info:")
+        print(f"{Fore.CYAN}Exploratory Data Analysis:{Style.RESET_ALL}")
+        print(f"{Fore.GREEN}Dataset shape: {df.shape}{Style.RESET_ALL}")
+        print(f"{Fore.CYAN}Dataset info:{Style.RESET_ALL}")
         df.info()
-        print("\nSummary statistics:")
+        print(f"{Fore.CYAN}Summary statistics:{Style.RESET_ALL}")
         print(df.describe())
-        print("\nMissing values:")
+        print(f"{Fore.CYAN}Missing values:{Style.RESET_ALL}")
         print(df.isnull().sum())
-        print("\nData types:")
+        print(f"{Fore.CYAN}Data types:{Style.RESET_ALL}")
         print(df.dtypes)
-        print("\nUnique values in each column:")
+        print(f"{Fore.CYAN}Unique values in each column:{Style.RESET_ALL}")
         for col in df.columns:
-            print(f"{col}: {df[col].nunique()}")
-        
+            print(f"{Fore.GREEN}{col}: {df[col].nunique()}{Style.RESET_ALL}")
+
         # Additional EDA steps
-        print("\nCorrelation matrix:")
+        print(f"{Fore.CYAN}Correlation matrix:{Style.RESET_ALL}")
         corr_matrix = df.select_dtypes(include=[np.number]).corr()
         print(corr_matrix)
-        
+
         # Identify highly correlated features
         high_corr = np.where(np.abs(corr_matrix) > 0.8)
         high_corr_list = [(corr_matrix.index[x], corr_matrix.columns[y]) for x, y in zip(*high_corr) if x != y and x < y]
         if high_corr_list:
-            print("\nHighly correlated features:")
+            print(f"{Fore.YELLOW}Highly correlated features:{Style.RESET_ALL}")
             for feat1, feat2 in high_corr_list:
-                print(f"{feat1} - {feat2}: {corr_matrix.loc[feat1, feat2]:.2f}")
-        
+                print(f"{Fore.GREEN}{feat1} - {feat2}: {corr_matrix.loc[feat1, feat2]:.2f}{Style.RESET_ALL}")
+
         # Identify potential outliers
-        print("\nPotential outliers (values beyond 3 standard deviations):")
+        print(f"{Fore.CYAN}Potential outliers (values beyond 3 standard deviations):{Style.RESET_ALL}")
         numeric_cols = df.select_dtypes(include=[np.number]).columns
         for col in numeric_cols:
             mean = df[col].mean()
             std = df[col].std()
-            outliers = df[(df[col] < mean - 3*std) | (df[col] > mean + 3*std)]
+            outliers = df[(df[col] < mean - 3 * std) | (df[col] > mean + 3 * std)]
             if not outliers.empty:
-                print(f"{col}: {len(outliers)} potential outliers")
-        
+                print(f"{Fore.GREEN}{col}: {len(outliers)} potential outliers{Style.RESET_ALL}")
+
         # Class distribution for the target variable (assuming last column is target)
         target_col = df.columns[-1]
-        print(f"\nClass distribution for target variable '{target_col}':")
-        print(df[target_col].value_counts(normalize=True))
\ No newline at end of file
+        print(f"{Fore.CYAN}Class distribution for target variable '{target_col}':{Style.RESET_ALL}")
+        print(df[target_col].value_counts(normalize=True))