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#!/usr/bin/env python3
"""
NSW Property Sales Data Analysis
Analyzes Valuer General NSW sales data to identify investment hotspots and trends
"""

import pandas as pd
import numpy as np
import matplotlib.pyplot as plt
import seaborn as sns

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import os
from slack_sdk.web import WebClient
from slack_sdk.socket_mode import SocketModeClient
from slack_sdk.socket_mode.response import SocketModeResponse
from slack_sdk.socket_mode.request import SocketModeRequest
import dotenv
import logging
from src.utils import (
    get_thread,
    apply_chat_template,

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{
 "cells": [
  {
   "cell_type": "markdown",
   "id": "abstract-destruction",
   "metadata": {},
   "source": [
    "# Exploratory Data Analysis (EDA) on Property Sales Dataset\n",
    "\n",
    "This Jupyter notebook presents an exploratory data analysis (EDA) of a property sales dataset. <br/>\n",

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{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "3344492e",
   "metadata": {},
   "outputs": [],
   "source": [
    "# get dataset\n",

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class FeatureExtractor(nn.Module):
    def __init__(self, model, layer_names):
        super().__init__()
        self.model = model
        self.layer_names = layer_names
        self._features = defaultdict(list)
        
        layer_dict = dict([*self.model.named_modules()])
        for layer_name in layer_names:
            layer = layer_dict[layer_name]