Add Business Performance Forecasting Model

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

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"
+    }
+  }
+}

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

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