Added startup profit predition

Prediction Models/Startup-profit-prediction/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

Prediction Models/Startup-profit-prediction/Readme.md

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+## **Startup Profit Prediction**
+**GOAL**
+
+The goal of this project is to analyze and predict the profit of a startup using features such as 'R&D Spend', 'Administration', 'Marketing Spend', 'State', etc. By leveraging multiple regression techniques, this project aims to identify the most significant factors influencing startup profitability and build a robust predictive model.
+
+**DATASET**
+
+Dataset can be downloaded from [here](https://www.kaggle.com/sonalisingh1411/startup50).
+
+**LIBRARIES NEEDED**
+- pandas
+- NumPy
+- Matplotlib
+- sklearn (For data training, importing models and performance check)
+
+
+**CONCLUSION**
+
+* The analysis of the startup dataset reveals significant correlations between the features and the profits, providing valuable insights for potential investors and decision-makers.

Prediction Models/Startup-profit-prediction/app.py

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+from flask import Flask, redirect, render_template, url_for, request
+import numpy as np
+import pickle
+
+regressor = pickle.load(open('startup.pkl', 'rb'))
+app = Flask(__name__)
+
+
+@app.route('/')
+def home():
+    return render_template("home.html")
+
+
+@app.route('/submit', methods=['POST', 'GET'])
+def submit():
+    if request.method == "POST":
+        state = request.form["state"]
+        rdspend = float(request.form["rdspend"])
+        adspend = float(request.form["adspend"])
+        mkspend = float(request.form["mkspend"])
+        if state == "New York":
+            state_list = [0.0, 1.0]
+        elif state == "California":
+            state_list = [0.0, 0.0]
+        else:
+            state_list = [1.0, 0.0]
+
+        input = np.array(state_list+[rdspend, adspend, mkspend])
+        input = input.reshape(1, len(input))
+        pred = regressor.predict(input)[0]
+
+    return render_template("output.html", pred=pred)
+
+
+if __name__ == "__main__":
+    app.run(debug=True)