diff --git a/App.py b/App.py
index 8db18a02..4e156490 100644
--- a/App.py
+++ b/App.py
@@ -176,6 +176,22 @@
             Ideal for travel, business meetings, and language learning, breaking down language barriers effortlessly.
         """
     },
+    {
+        "name": "Business Performance Forecaster",
+        "description": "Forecast business profits based on various investment areas for better financial planning and budget allocation.",
+        "details": """
+            ### Overview  
+            The Business Performance Forecaster predicts company profit based on investment in R&D, administration, and marketing, using machine learning to analyze investment patterns and optimize budget allocation.
+
+            ### Key Features  
+            - **Profit Prediction**: Provides an estimated profit based on investment data.
+            - **Investment Analysis**: Evaluates how different spending areas impact overall profit.
+            - **Multi-Input Support**: Accounts for multiple variables like R&D, administration, and marketing expenses.
+
+            ### Use Cases  
+            Useful for companies looking to plan budgets, assess the impact of investments, and improve decision-making processes in financial forecasting.
+        """
+    }
 ]
 
 # Define shades of blue for calculators
diff --git a/form_configs/business_performance_forecasting.json b/form_configs/business_performance_forecasting.json
new file mode 100644
index 00000000..59711743
--- /dev/null
+++ b/form_configs/business_performance_forecasting.json
@@ -0,0 +1,31 @@
+{
+  "Business Forecast Form": {
+    "R&D Spend": {
+      "type": "number",
+      "min_value": 0.0,
+      "default_value": 100000.0,
+      "step": 1000.0,
+      "field_name": "RnD_Spend"
+    },
+    "Administration": {
+      "type": "number",
+      "min_value": 0.0,
+      "default_value": 50000.0,
+      "step": 1000.0,
+      "field_name": "Administration"
+    },
+    "Marketing Spend": {
+      "type": "number",
+      "min_value": 0.0,
+      "default_value": 100000.0,
+      "step": 1000.0,
+      "field_name": "Marketing_Spend"
+    },
+    "State": {
+      "type": "dropdown",
+      "options": ["New York", "California", "Florida"],
+      "default_value": "New York",
+      "field_name": "State"
+    }
+  }
+}
diff --git a/models/business_performance_forecasting/data/50_Startups.csv b/models/business_performance_forecasting/data/50_Startups.csv
new file mode 100644
index 00000000..b1cc5f20
--- /dev/null
+++ b/models/business_performance_forecasting/data/50_Startups.csv
@@ -0,0 +1,51 @@
+R&D Spend,Administration,Marketing Spend,State,Profit
+165349.2,136897.8,471784.1,New York,192261.83
+162597.7,151377.59,443898.53,California,191792.06
+153441.51,101145.55,407934.54,Florida,191050.39
+144372.41,118671.85,383199.62,New York,182901.99
+142107.34,91391.77,366168.42,Florida,166187.94
+131876.9,99814.71,362861.36,New York,156991.12
+134615.46,147198.87,127716.82,California,156122.51
+130298.13,145530.06,323876.68,Florida,155752.6
+120542.52,148718.95,311613.29,New York,152211.77
+123334.88,108679.17,304981.62,California,149759.96
+101913.08,110594.11,229160.95,Florida,146121.95
+100671.96,91790.61,249744.55,California,144259.4
+93863.75,127320.38,249839.44,Florida,141585.52
+91992.39,135495.07,252664.93,California,134307.35
+119943.24,156547.42,256512.92,Florida,132602.65
+114523.61,122616.84,261776.23,New York,129917.04
+78013.11,121597.55,264346.06,California,126992.93
+94657.16,145077.58,282574.31,New York,125370.37
+91749.16,114175.79,294919.57,Florida,124266.9
+86419.7,153514.11,0,New York,122776.86
+76253.86,113867.3,298664.47,California,118474.03
+78389.47,153773.43,299737.29,New York,111313.02
+73994.56,122782.75,303319.26,Florida,110352.25
+67532.53,105751.03,304768.73,Florida,108733.99
+77044.01,99281.34,140574.81,New York,108552.04
+64664.71,139553.16,137962.62,California,107404.34
+75328.87,144135.98,134050.07,Florida,105733.54
+72107.6,127864.55,353183.81,New York,105008.31
+66051.52,182645.56,118148.2,Florida,103282.38
+65605.48,153032.06,107138.38,New York,101004.64
+61994.48,115641.28,91131.24,Florida,99937.59
+61136.38,152701.92,88218.23,New York,97483.56
+63408.86,129219.61,46085.25,California,97427.84
+55493.95,103057.49,214634.81,Florida,96778.92
+46426.07,157693.92,210797.67,California,96712.8
+46014.02,85047.44,205517.64,New York,96479.51
+28663.76,127056.21,201126.82,Florida,90708.19
+44069.95,51283.14,197029.42,California,89949.14
+20229.59,65947.93,185265.1,New York,81229.06
+38558.51,82982.09,174999.3,California,81005.76
+28754.33,118546.05,172795.67,California,78239.91
+27892.92,84710.77,164470.71,Florida,77798.83
+23640.93,96189.63,148001.11,California,71498.49
+15505.73,127382.3,35534.17,New York,69758.98
+22177.74,154806.14,28334.72,California,65200.33
+1000.23,124153.04,1903.93,New York,64926.08
+1315.46,115816.21,297114.46,Florida,49490.75
+0,135426.92,0,California,42559.73
+542.05,51743.15,0,New York,35673.41
+0,116983.8,45173.06,California,14681.4
\ No newline at end of file
diff --git a/models/business_performance_forecasting/model.py b/models/business_performance_forecasting/model.py
new file mode 100644
index 00000000..691281f3
--- /dev/null
+++ b/models/business_performance_forecasting/model.py
@@ -0,0 +1,14 @@
+import pickle
+import os
+model_path = os.path.join(os.path.dirname(__file__), 'saved_models', 'model.pkl')
+scaler_path = os.path.join(os.path.dirname(__file__), 'saved_models', 'scaler.pkl')
+
+
+# Load the saved model and scaler
+def load_model_and_scaler():
+    with open(model_path, 'rb') as model_file:
+        model = pickle.load(model_file)
+    with open(scaler_path, 'rb') as scaler_file:
+        scaler = pickle.load(scaler_file)
+
+    return model, scaler
diff --git a/models/business_performance_forecasting/notebooks/Business_forecasting.py b/models/business_performance_forecasting/notebooks/Business_forecasting.py
new file mode 100644
index 00000000..ae052b4c
--- /dev/null
+++ b/models/business_performance_forecasting/notebooks/Business_forecasting.py
@@ -0,0 +1,43 @@
+import numpy as np 
+import pandas as pd 
+import pickle
+import os
+
+# Load the data
+df = pd.read_csv('50_Startups.csv')
+X = df.iloc[:, :-1].values
+y = df.iloc[:, -1].values
+
+# Preprocessing - Encoding categorical data
+from sklearn.compose import ColumnTransformer
+from sklearn.preprocessing import OneHotEncoder
+ct = ColumnTransformer(transformers=[('encoder', OneHotEncoder(), [3])], remainder='passthrough')
+X = np.array(ct.fit_transform(X))
+
+# Splitting the dataset
+from sklearn.model_selection import train_test_split
+X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=0)
+
+# Training the model
+from sklearn.linear_model import LinearRegression
+model = LinearRegression()
+model.fit(X_train, y_train)
+
+# Make predictions
+y_pred = model.predict(X_test)
+
+# Print predictions alongside actual values
+np.set_printoptions(precision=2)
+print(np.concatenate((y_pred.reshape(len(y_pred),1), y_test.reshape(len(y_test),1)), axis=1))
+
+model_path = os.path.abspath("model.pkl")
+scaler_path = os.path.abspath("scaler.pkl")
+
+# Save the model and preprocessing objects
+with open(model_path, 'wb') as model_file:
+    pickle.dump(model, model_file)
+
+with open(scaler_path, 'wb') as scaler_file:
+    pickle.dump(ct, scaler_file)
+
+print("Model and preprocessing objects saved successfully!")
diff --git a/models/business_performance_forecasting/predict.py b/models/business_performance_forecasting/predict.py
new file mode 100644
index 00000000..7298c9be
--- /dev/null
+++ b/models/business_performance_forecasting/predict.py
@@ -0,0 +1,20 @@
+# import os
+import numpy as np
+from models.business_performance_forecasting.model import load_model_and_scaler  # Import the function from model.py
+
+# Define the prediction function
+def get_prediction(RnD_Spend, Administration, Marketing_Spend, State):
+    # Load the model and scaler
+    model, scaler = load_model_and_scaler()
+    # Prepare input features as a NumPy array
+    input_data = np.array([[RnD_Spend, Administration, Marketing_Spend, State]])
+    
+    # Apply the scaler
+    scaled_data = scaler.transform(input_data)
+    scaled_data = scaled_data.astype(float)  
+    
+    # Make prediction using the loaded model
+    prediction = model.predict(scaled_data)
+    
+    return prediction[0]  # Return the predicted profit
+
diff --git a/models/business_performance_forecasting/saved_models/model.pkl b/models/business_performance_forecasting/saved_models/model.pkl
new file mode 100644
index 00000000..3786fb97
Binary files /dev/null and b/models/business_performance_forecasting/saved_models/model.pkl differ
diff --git a/models/business_performance_forecasting/saved_models/scaler.pkl b/models/business_performance_forecasting/saved_models/scaler.pkl
new file mode 100644
index 00000000..9345e923
Binary files /dev/null and b/models/business_performance_forecasting/saved_models/scaler.pkl differ
diff --git a/pages/Business_Performance_Forecasting.py b/pages/Business_Performance_Forecasting.py
new file mode 100644
index 00000000..2b413057
--- /dev/null
+++ b/pages/Business_Performance_Forecasting.py
@@ -0,0 +1,4 @@
+from page_handler import PageHandler
+
+page_handler = PageHandler("pages/pages.json")
+page_handler.render_page("Business Performance Forecasting")
diff --git a/pages/pages.json b/pages/pages.json
index cc76fe56..8143915d 100644
--- a/pages/pages.json
+++ b/pages/pages.json
@@ -195,7 +195,30 @@
         "description": "This model uses a dataset containing demographic and health-related factors to predict the cost of insurance. The features include age, sex, BMI, children, smoker status, and region, with predictions made using the Random Forest algorithm for accurate results. Ensemble techniques like XGBoost will also be used to further enhance the prediction accuracy."
       }
     ]
+  },
+  "Business Performance Forecasting": {
+    "title": "Business Performance Forecasting",
+    "page_title": "Business Performance Forecasting",
+    "page_icon": "\ud83c\udf3e",
+    "model_predict_file_path": "models/business_performance_forecasting/predict.py",
+    "model_function": "get_prediction",
+    "model_detail_function": "model_details",
+    "form_config_path": "form_configs/business_performance_forecasting.json",
+    "tabs": [
+      {
+        "name": "Business Forecast Form",
+        "type": "form",
+        "form_name": "Business Forecast Form"
+      },
+      {
+        "name": "Model Details",
+        "type": "model_details",
+        "problem_statement": "The Business Performance Forecasting model predicts future profits based on R&D spend, administration costs, marketing spend, and state. By utilizing machine learning, this tool assists businesses in making informed decisions about resource allocation.",
+        "description": "This model employs a dataset with features including R&D spend, administration costs, marketing spend, and geographic location to forecast profits. The predictions are generated using regression techniques, ensuring accuracy and reliability for business strategy planning."
+      }
+    ]
   }
+
 }