Upload 4 files

Pop_Data.csv

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+place,url,scrape_time,day_of_week,hour_of_day,popularity_percent_normal,Base_Price
+AnRYn1F8NfSGLexf7.20200318_161636,https://goo.gl/maps/AnRYn1F8NfSGLexf7,20200318_161636,Monday,0,24,200
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app.py

@@ -0,0 +1,27 @@
+from flask import Flask, request, jsonify
+import dynamic_pricing 
+import joblib
+
+app = Flask(__name__)
+
+loaded_rf_model = joblib.load("random_forest_model.pkl")
+
+@app.route('/', methods=['POST'])
+def predict_amt():
+    try:
+        # Get data from the request
+        data = request.get_json()
+
+        # Use your machine learning model to make predictions
+        prediction = loaded_rf_model.predict(data)  # Replace with your model code
+    
+        # Return the prediction as a JSON response
+        return jsonify({'prediction': prediction})
+    
+    except Exception as e:
+        return jsonify({'error': str(e)}), 400
+
+
+if __name__ == "__main__":
+     app.run(debug=True)
+

dynamic_pricing.py

@@ -0,0 +1,114 @@
+# -*- coding: utf-8 -*-
+"""dynamic pricing.ipynb
+
+Automatically generated by Colaboratory.
+
+Original file is located at
+    https://colab.research.google.com/drive/1pMuvzwELNm1DsTdL5dfBdA2HCjB6uwgh
+"""
+
+# Commented out IPython magic to ensure Python compatibility.
+import datetime
+import joblib
+import numpy as np
+import pandas as pd
+
+import matplotlib.pyplot as plt
+import seaborn as sns
+# %matplotlib inline
+
+from sklearn.model_selection import train_test_split
+from sklearn.linear_model import LinearRegression
+from sklearn.ensemble import RandomForestRegressor
+from sklearn.preprocessing import LabelEncoder
+from sklearn.preprocessing import StandardScaler
+from sklearn.metrics import r2_score
+
+dataset = pd.read_csv("Pop_Data.csv")
+dataset.head(5)
+
+label_encoder = LabelEncoder()
+dataset['day_of_week'] = label_encoder.fit_transform(dataset['day_of_week'])
+
+X_train, X_test, y_train, y_test = train_test_split(dataset.iloc[:, :-1],
+                                                    dataset.iloc[:, -1],
+                                                    test_size = 0.3,
+                                                    random_state = 42)
+
+X_train.info()
+
+"""# EDA"""
+
+X_train = X_train.iloc[:, 3:]
+X_test = X_test.iloc[:, 3:]
+
+X_train.info
+
+plt.figure(figsize = (12, 8))
+plot = sns.countplot(x = 'day_of_week', data = X_train)
+plt.xticks(rotation = 90)
+for p in plot.patches:
+    plot.annotate(p.get_height(),
+                        (p.get_x() + p.get_width() / 2.0,
+                         p.get_height()),
+                        ha = 'center',
+                        va = 'center',
+                        xytext = (0, 5),
+                        textcoords = 'offset points')
+
+plt.title("Price changes based on day")
+plt.xlabel("Day")
+plt.ylabel("Price")
+
+print(sum(X_train["day_of_week"].isnull()))
+print(sum(X_test["day_of_week"].isnull()))
+
+print(sum(X_train["hour_of_day"].isnull()))
+print(sum(X_test["hour_of_day"].isnull()))
+
+print(sum(X_train["popularity_percent_normal"].isnull()))
+print(sum(X_test["popularity_percent_normal"].isnull()))
+
+X_train["popularity_percent_normal"].fillna(X_train["popularity_percent_normal"].astype("float64").mean(), inplace = True)
+
+# X_train = pd.get_dummies(X_train,
+#                          columns = ["day_of_week"],
+#                          drop_first = True)
+
+# X_test = pd.get_dummies(X_test,
+#                          columns = ["day_of_week"],
+#                          drop_first = True)
+
+# missing_cols = set(X_train.columns) - set(X_test.columns)
+# for col in missing_cols:
+#     X_test[col] = 0
+# X_test = X_test[X_train.columns]
+
+standardScaler = StandardScaler()
+standardScaler.fit(X_train)
+X_train = standardScaler.transform(X_train)
+X_test = standardScaler.transform(X_test)
+
+linearRegression = LinearRegression()
+linearRegression.fit(X_train, y_train)
+y_pred = linearRegression.predict(X_test)
+r2_score(y_test, y_pred)
+
+rf = RandomForestRegressor(n_estimators = 100)
+rf.fit(X_train, y_train)
+y_pred = rf.predict(X_test)
+r2_score(y_test, y_pred)
+
+def save_model(model, filename):
+    joblib.dump(model, filename)
+
+# Save the model
+save_model(rf, "random_forest_model.pkl")
+
+def predict(data):
+    dataArr = list(data)
+    day_of_week_encoded = label_encoder.fit_transform([dataArr[0]])[0]
+    datapoint = [day_of_week_encoded, dataArr[1], dataArr[2]]
+    npArr = np.asarray(datapoint).reshape(1,-1)
+    x_test = standardScaler.fit_transform(npArr)
+    return rf.predict(x_test)

requirements.txt

@@ -0,0 +1,7 @@
+pip==23.2.1
+pandas==1.3.3
+matplotlib==3.4.3
+numpy==1.21.2
+seaborn==0.11.2
+scikit-learn==0.24.2
+Flask==2.1.1