abhijeet-talaulikar

# Response function - for questions identified in text
def generate_default_response(documents, prompt=prompt_default):
  qa = get_guery_function(documents)
  try:
      llm_response = qa(prompt)
      response = llm_response["result"]
  except Exception as err:
      response = str(err)
  return response

abhijeet-talaulikar

op_custom = st.container()

def updateChatBox():
  if st.session_state['response_default']:
    frm_ask = op_custom.form('frm_ask')
    question = frm_ask.text_area('If you have any specific question, ask here.', 'What are the three takeaways from this quarter?')
    frm_ask_submitted = frm_ask.form_submit_button('Ask')
    if frm_ask_submitted:
      if not openai_api_key.startswith('sk-'):
        op_custom.warning('Please enter your OpenAI API key!', icon='⚠')

abhijeet-talaulikar

import numpy as np
import pandas as pd

campaign_data = pd.read_csv("cashback_activation_data.csv")

campaign_data = campaign_data.sort_values(['customer_id', 'timestamp'],
                    ascending=[False, True])

campaign_data['visit_order'] = campaign_data.groupby('customer_id').cumcount() + 1

abhijeet-talaulikar

# Step 1: Convert all interactions to a list
journeys = campaign_data.groupby('customer_id')['channel'].aggregate(
    lambda x: x.tolist()).reset_index()

# Step 2: Add last interaction as 1 or 0 event representing activation
activation_results = campaign_data.drop_duplicates('customer_id', keep='last')[['customer_id', 'activation']]
journeys = pd.merge(journeys, activation_results, how='left', on='customer_id')

# Step 3: Add start and end states based on whether customer activated
journeys['path'] = np.where(

abhijeet-talaulikar

# Function to create intermediate path strings
def transition_states(paths):
    unique_channels = set(x for element in paths for x in element)
    transition_states = {x + '>' + y: 0 for x in unique_channels for y in unique_channels}

    for possible_state in unique_channels:
        if possible_state not in ['Activation', 'Null']:
            for user_path in paths:
                if possible_state in user_path:
                    indices = [i for i, s in enumerate(user_path) if possible_state in s]

abhijeet-talaulikar

import matplotlib.pyplot as plt
import seaborn as sns
from markovchain import MarkovChain

mc = MarkovChain(trans_matrix.values, trans_matrix.index)
mc.draw()

abhijeet-talaulikar

def removal_effects(df, conversion_rate):
    removal_effects_dict = {}
    channels = [channel for channel in df.columns if channel not in ['Start',
                                                                     'Null',
                                                                     'Activation']]
    for channel in channels:
        removal_df = df.drop(channel, axis=1).drop(channel, axis=0)
        for column in removal_df.columns:
            row_sum = np.sum(list(removal_df.loc[column]))
            null_pct = float(1) - row_sum

abhijeet-talaulikar

df_multi = pd.DataFrame({
    'Channel': attributions.keys(),
    'Attribution style': 'Journey',
    'Activations': attributions.values()
})

df_first = pd.DataFrame({
    'Channel': attributions.keys(),
    'Attribution style': 'First touchpoint'
})

abhijeet-talaulikar

df_scatter = df_multi.copy()

df_scatter['Coverage'] = df_scatter['Channel'].map(
    campaign_data.groupby('channel')['customer_id'].nunique().to_dict()
)

df_scatter['Total Clicks'] = df_scatter['Channel'].map(
    journeys['path'].apply(lambda x: x[-2]).value_counts().to_dict()
)

abhijeet-talaulikar

plt.figure(figsize=(10,5))
sns.scatterplot(data=df_scatter, x='Click Activation Rate', y='Activation Rate', s=200, color='#2653de')

for line in range(0, df_scatter.shape[0]):
     plt.text(df_scatter['Click Activation Rate'][line]+0.001, df_scatter['Activation Rate'][line], 
     df_scatter['Channel'][line], horizontalalignment='left', 
     size='medium', color='black', weight='semibold')