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Added model.py

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zgolebiewska 2024-04-30 10:49:32 +02:00
parent 61212f5e4c
commit 70f8d14fb8
4 changed files with 195 additions and 2 deletions
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6
Dockerfile
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@ -2,9 +2,13 @@ FROM ubuntu:latest
RUN apt-get update && apt-get install -y python3-pip unzip coreutils
RUN pip install --user kaggle pandas
RUN pip install --user kaggle pandas scikit-learn tensorflow
WORKDIR /app
COPY ./data_processing.sh ./
COPY ./OrangeQualityData.csv ./
COPY ./orange_quality_model_tf.h5 ./
COPY ./predictions_tf.json ./
CMD ["python3", "data_processing.sh"]

40
model.py Normal file
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@ -0,0 +1,40 @@
import tensorflow as tf
import pandas as pd
from sklearn.preprocessing import LabelEncoder, StandardScaler
from sklearn.model_selection import train_test_split
import json
df = pd.read_csv('OrangeQualityData.csv')
encoder = LabelEncoder()
df["Color"] = encoder.fit_transform(df["Color"])
df["Variety"] = encoder.fit_transform(df["Variety"])
df["Blemishes"] = df["Blemishes (Y/N)"].apply(lambda x: 1 if x.startswith("Y") else 0)
df.drop(columns=["Blemishes (Y/N)"], inplace=True)
X = df.drop(columns=["Quality (1-5)"])
y = df["Quality (1-5)"]
X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=42)
scaler = StandardScaler()
X_train_scaled = scaler.fit_transform(X_train)
X_test_scaled = scaler.transform(X_test)
model = tf.keras.Sequential([
tf.keras.layers.Dense(64, activation='relu', input_shape=(X_train_scaled.shape[1],)),
tf.keras.layers.Dense(32, activation='relu'),
tf.keras.layers.Dense(1)
])
model.compile(optimizer='sgd', loss='mse')
history = model.fit(X_train_scaled, y_train, epochs=100, verbose=0, validation_data=(X_test_scaled, y_test))
model.save('orange_quality_model_tf.h5')
predictions = model.predict(X_test_scaled)
with open('predictions_tf.json', 'w') as f:
json.dump(predictions.tolist(), f, indent=4)

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orange_quality_model_tf.h5 Normal file
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predictions_tf.json Normal file
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