Build Custom AI Tools for Your AI Agents that Combine Machine Learning and Statistical Analysis

class IntelligentDataAnalyzer(BaseTool):
   name: str = "intelligent_data_analyzer"
   description: str = "Advanced data analysis tool that performs statistical analysis, machine learning clustering, outlier detection, correlation analysis, and generates visualizations with actionable insights."
   args_schema: type[BaseModel] = DataAnalysisInput
   response_format: str = "content_and_artifact"
  
   def _run(self, data: List[Dict], analysis_type: str = "comprehensive", target_column: Optional[str] = None, max_clusters: int = 5) -> Tuple[str, Dict]:
       try:
           df = pd.DataFrame(data)
           if df.empty:
               raise ToolException("Dataset is empty")
          
           insights = {"dataset_info": self._get_dataset_info(df)}
          
           if analysis_type in ["comprehensive", "correlation"]:
               insights["correlation_analysis"] = self._correlation_analysis(df)
           if analysis_type in ["comprehensive", "clustering"]:
               insights["clustering_analysis"] = self._clustering_analysis(df, max_clusters)
           if analysis_type in ["comprehensive", "outlier"]:
               insights["outlier_detection"] = self._outlier_detection(df)
          
           if target_column and target_column in df.columns:
               insights["target_analysis"] = self._target_analysis(df, target_column)
          
           recommendations = self._generate_recommendations(df, insights)
           summary = self._create_analysis_summary(insights, recommendations)
          
           artifact = {
               "insights": insights,
               "recommendations": recommendations,
               "data_shape": df.shape,
               "analysis_type": analysis_type,
               "numeric_columns": df.select_dtypes(include=[np.number]).columns.tolist(),
               "categorical_columns": df.select_dtypes(include=['object']).columns.tolist()
           }
          
           return summary, artifact
          
       except Exception as e:
           raise ToolException(f"Analysis failed: {str(e)}")
  
   def _get_dataset_info(self, df: pd.DataFrame) -> Dict:
       return {
           "shape": df.shape,
           "columns": df.columns.tolist(),
           "dtypes": df.dtypes.astype(str).to_dict(),
           "missing_values": df.isnull().sum().to_dict(),
           "memory_usage": df.memory_usage(deep=True).sum()
       }
  
   def _correlation_analysis(self, df: pd.DataFrame) -> Dict:
       numeric_df = df.select_dtypes(include=[np.number])
       if numeric_df.empty:
           return {"message": "No numeric columns for correlation analysis"}
      
       corr_matrix = numeric_df.corr()
       strong_corr = []
       for i in range(len(corr_matrix.columns)):
           for j in range(i+1, len(corr_matrix.columns)):
               corr_val = corr_matrix.iloc[i, j]
               if abs(corr_val) > 0.7:
                   strong_corr.append({"var1": corr_matrix.columns[i], "var2": corr_matrix.columns[j], "correlation": round(corr_val, 3)})
      
       return {
           "correlation_matrix": corr_matrix.round(3).to_dict(),
           "strong_correlations": strong_corr,
           "avg_correlation": round(corr_matrix.values[np.triu_indices_from(corr_matrix.values, k=1)].mean(), 3)
       }
  
   def _clustering_analysis(self, df: pd.DataFrame, max_clusters: int) -> Dict:
       numeric_df = df.select_dtypes(include=[np.number]).dropna()
       if numeric_df.shape[0] < 2 or numeric_df.shape[1] < 2:
           return {"message": "Insufficient numeric data for clustering"}
      
       scaler = StandardScaler()
       scaled_data = scaler.fit_transform(numeric_df)
      
       inertias = []
       K_range = range(1, min(max_clusters + 1, len(numeric_df) // 2 + 1))
      
       for k in K_range:
           kmeans = KMeans(n_clusters=k, random_state=42, n_init=10)
           kmeans.fit(scaled_data)
           inertias.append(kmeans.inertia_)
      
       optimal_k = self._find_elbow_point(inertias, K_range)
       kmeans = KMeans(n_clusters=optimal_k, random_state=42, n_init=10)
       cluster_labels = kmeans.fit_predict(scaled_data)
      
       cluster_stats = {}
       for i in range(optimal_k):
           cluster_data = numeric_df[cluster_labels == i]
           cluster_stats[f"cluster_{i}"] = {
               "size": len(cluster_data),
               "percentage": round(len(cluster_data) / len(numeric_df) * 100, 1),
               "means": cluster_data.mean().round(3).to_dict()
           }
      
       return {
           "optimal_clusters": optimal_k,
           "cluster_stats": cluster_stats,
           "silhouette_score": round(silhouette_score(scaled_data, cluster_labels), 3) if len(set(cluster_labels)) > 1 else 0.0,
           "inertias": inertias
       }
  
   def _outlier_detection(self, df: pd.DataFrame) -> Dict:
       numeric_df = df.select_dtypes(include=[np.number])
       if numeric_df.empty:
           return {"message": "No numeric columns for outlier detection"}
      
       outliers = {}
       for col in numeric_df.columns:
           data = numeric_df[col].dropna()
           Q1, Q3 = data.quantile(0.25), data.quantile(0.75)
           IQR = Q3 - Q1
           iqr_outliers = data[(data < Q1 - 1.5 * IQR) | (data > Q3 + 1.5 * IQR)]
           z_scores = np.abs((data - data.mean()) / data.std())
           z_outliers = data[z_scores > 3]
          
           outliers[col] = {
               "iqr_outliers": len(iqr_outliers),
               "z_score_outliers": len(z_outliers),
               "outlier_percentage": round(len(iqr_outliers) / len(data) * 100, 2)
           }
      
       return outliers
  
   def _target_analysis(self, df: pd.DataFrame, target_col: str) -> Dict:
       if target_col not in df.columns:
           return {"error": f"Column {target_col} not found"}
      
       target_data = df[target_col].dropna()
      
       if pd.api.types.is_numeric_dtype(target_data):
           return {
               "type": "numeric",
               "stats": {
                   "mean": round(target_data.mean(), 3),
                   "median": round(target_data.median(), 3),
                   "std": round(target_data.std(), 3),
                   "skewness": round(target_data.skew(), 3),
                   "kurtosis": round(target_data.kurtosis(), 3)
               },
               "distribution": "normal" if abs(target_data.skew()) < 0.5 else "skewed"
           }
       else:
           value_counts = target_data.value_counts()
           return {
               "type": "categorical",
               "unique_values": len(value_counts),
               "most_common": value_counts.head(5).to_dict(),
               "entropy": round(-sum((p := value_counts / len(target_data)) * np.log2(p + 1e-10)), 3)
           }
  
   def _generate_recommendations(self, df: pd.DataFrame, insights: Dict) -> List[str]:
       recommendations = []
      
       missing_pct = sum(insights["dataset_info"]["missing_values"].values()) / (df.shape[0] * df.shape[1]) * 100
       if missing_pct > 10:
           recommendations.append(f"Consider data imputation - {missing_pct:.1f}% missing values detected")
      
       if "correlation_analysis" in insights and insights["correlation_analysis"].get("strong_correlations"):
           recommendations.append("Strong correlations detected - consider feature selection or dimensionality reduction")
      
       if "clustering_analysis" in insights:
           cluster_info = insights["clustering_analysis"]
           if isinstance(cluster_info, dict) and "optimal_clusters" in cluster_info:
               recommendations.append(f"Data segments into {cluster_info['optimal_clusters']} distinct groups - useful for targeted strategies")
      
       if "outlier_detection" in insights:
           high_outlier_cols = [col for col, info in insights["outlier_detection"].items() if isinstance(info, dict) and info.get("outlier_percentage", 0) > 5]
           if high_outlier_cols:
               recommendations.append(f"High outlier percentage in: {', '.join(high_outlier_cols)} - investigate data quality")
      
       return recommendations if recommendations else ["Data appears well-structured with no immediate concerns"]
  
   def _create_analysis_summary(self, insights: Dict, recommendations: List[str]) -> str:
       dataset_info = insights["dataset_info"]
       summary = f"""📊 INTELLIGENT DATA ANALYSIS COMPLETE


Dataset Overview: {dataset_info['shape'][0]} rows × {dataset_info['shape'][1]} columns
Numeric Features: {len([c for c, t in dataset_info['dtypes'].items() if 'int' in t or 'float' in t])}
Categorical Features: {len([c for c, t in dataset_info['dtypes'].items() if 'object' in t])}


Key Insights Generated:
• Statistical correlations and relationships identified
• Clustering patterns discovered for segmentation
• Outlier detection completed for data quality assessment
• Feature importance and distribution analysis performed


Top Recommendations:
{chr(10).join('• ' + rec for rec in recommendations[:3])}


Analysis includes ML-powered clustering, statistical correlations, and actionable business insights."""
      
       return summary
  
   def _find_elbow_point(self, inertias: List[float], k_range: range) -> int:
       if len(inertias) < 3:
           return list(k_range)[0]
       diffs = [inertias[i-1] - inertias[i] for i in range(1, len(inertias))]
       return list(k_range)[diffs.index(max(diffs)) + 1] if diffs else list(k_range)[0]