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razorpod-ad-ctr

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Add initial files with model

322ebec over 1 year ago

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# Abhishek
# Razorpod
# Loading Dataset for training and calling dataframes
import pandas as pd
from sklearn.preprocessing import LabelEncoder

# Load dataset
df = pd.read_csv("ad_ctr_sample_data.csv")

# Encode categorical variables
encoder = LabelEncoder()
df["Ad Copy"] = encoder.fit_transform(df["Ad Copy"])
df["Image Type"] = encoder.fit_transform(df["Image Type"])
df["CTA"] = encoder.fit_transform(df["CTA"])
df["Target Audience"] = encoder.fit_transform(df["Target Audience"])

# Feature engineering
df["Engagement Score"] = df["Clicks"] / df["Impressions"]

# Save processed data
df.to_csv("processed_ad_data.csv", index=False)
print("Data preprocessed and saved!")

	# Abhishek
	# Razorpod
	# Loading Dataset for training and calling dataframes
	import pandas as pd
	from sklearn.preprocessing import LabelEncoder

	# Load dataset
	df = pd.read_csv("ad_ctr_sample_data.csv")

	# Encode categorical variables
	encoder = LabelEncoder()
	df["Ad Copy"] = encoder.fit_transform(df["Ad Copy"])
	df["Image Type"] = encoder.fit_transform(df["Image Type"])
	df["CTA"] = encoder.fit_transform(df["CTA"])
	df["Target Audience"] = encoder.fit_transform(df["Target Audience"])

	# Feature engineering
	df["Engagement Score"] = df["Clicks"] / df["Impressions"]

	# Save processed data
	df.to_csv("processed_ad_data.csv", index=False)
	print("Data preprocessed and saved!")