diff --git "a/notebooks/financial_sentiment_analysis.ipynb" "b/notebooks/financial_sentiment_analysis.ipynb"
--- "a/notebooks/financial_sentiment_analysis.ipynb"
+++ "b/notebooks/financial_sentiment_analysis.ipynb"
@@ -22,7 +22,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 17,
+   "execution_count": 26,
    "id": "3038c1d8",
    "metadata": {},
    "outputs": [
@@ -68,7 +68,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 18,
+   "execution_count": 27,
    "id": "d0bb6ca4",
    "metadata": {},
    "outputs": [
@@ -106,7 +106,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 19,
+   "execution_count": 28,
    "id": "0d28dcf3",
    "metadata": {},
    "outputs": [
@@ -236,7 +236,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 29,
    "id": "45b2014d",
    "metadata": {},
    "outputs": [
@@ -245,19 +245,90 @@
      "output_type": "stream",
      "text": [
       "Fetching news articles...\n",
-      "Found 853 articles for 'AAPL'\n"
+      "Found 853 articles for 'AAPL'\n",
+      "Successfully fetched and converted 100 news articles to DataFrame.\n",
+      "Found 853 articles for 'AAPL'\n",
+      "Successfully fetched and converted 100 news articles to DataFrame.\n"
      ]
     },
     {
-     "ename": "AttributeError",
-     "evalue": "'list' object has no attribute 'empty'",
-     "output_type": "error",
-     "traceback": [
-      "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m",
-      "\u001b[1;31mAttributeError\u001b[0m                            Traceback (most recent call last)",
-      "Cell \u001b[1;32mIn[20], line 4\u001b[0m\n\u001b[0;32m      2\u001b[0m \u001b[38;5;28mprint\u001b[39m(\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mFetching news articles...\u001b[39m\u001b[38;5;124m\"\u001b[39m)\n\u001b[0;32m      3\u001b[0m news_df \u001b[38;5;241m=\u001b[39m get_news_articles(TICKER, START_DATE, END_DATE)\n\u001b[1;32m----> 4\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m news_df \u001b[38;5;129;01mis\u001b[39;00m \u001b[38;5;129;01mnot\u001b[39;00m \u001b[38;5;28;01mNone\u001b[39;00m \u001b[38;5;129;01mand\u001b[39;00m \u001b[38;5;129;01mnot\u001b[39;00m \u001b[43mnews_df\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mempty\u001b[49m:\n\u001b[0;32m      5\u001b[0m     \u001b[38;5;28mprint\u001b[39m(\u001b[38;5;124mf\u001b[39m\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mSuccessfully fetched \u001b[39m\u001b[38;5;132;01m{\u001b[39;00m\u001b[38;5;28mlen\u001b[39m(news_df)\u001b[38;5;132;01m}\u001b[39;00m\u001b[38;5;124m news articles.\u001b[39m\u001b[38;5;124m\"\u001b[39m)\n\u001b[0;32m      6\u001b[0m     display(news_df\u001b[38;5;241m.\u001b[39mhead())\n",
-      "\u001b[1;31mAttributeError\u001b[0m: 'list' object has no attribute 'empty'"
-     ]
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>date</th>\n",
+       "      <th>title</th>\n",
+       "      <th>description</th>\n",
+       "      <th>source</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>2025-04-14</td>\n",
+       "      <td>AAPL opens market at highest price since Trump...</td>\n",
+       "      <td>Apple opened trading on Monday, April 14, 2025...</td>\n",
+       "      <td>{'id': None, 'name': 'AppleInsider'}</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>2025-04-03</td>\n",
+       "      <td>Apple stock drops almost 10% on market open in...</td>\n",
+       "      <td>Apple is a US-based company that relies heavil...</td>\n",
+       "      <td>{'id': None, 'name': '9to5Mac'}</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>2025-04-25</td>\n",
+       "      <td>Mag 7 earnings, PCE data, April jobs report: W...</td>\n",
+       "      <td>Here's what investors are watching during the ...</td>\n",
+       "      <td>{'id': None, 'name': 'Yahoo Entertainment'}</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>2025-04-10</td>\n",
+       "      <td>AAPL crumble: stock hit again, as White House ...</td>\n",
+       "      <td>After a brief respite on Wednesday, Apple's st...</td>\n",
+       "      <td>{'id': None, 'name': 'AppleInsider'}</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4</th>\n",
+       "      <td>2025-04-28</td>\n",
+       "      <td>Consumers get ahead of tariffs, Morgan Stanley...</td>\n",
+       "      <td>Morgan Stanley hiked its Apple stock price tar...</td>\n",
+       "      <td>{'id': None, 'name': 'AppleInsider'}</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "         date                                              title                                        description                                       source\n",
+       "0  2025-04-14  AAPL opens market at highest price since Trump...  Apple opened trading on Monday, April 14, 2025...         {'id': None, 'name': 'AppleInsider'}\n",
+       "1  2025-04-03  Apple stock drops almost 10% on market open in...  Apple is a US-based company that relies heavil...              {'id': None, 'name': '9to5Mac'}\n",
+       "2  2025-04-25  Mag 7 earnings, PCE data, April jobs report: W...  Here's what investors are watching during the ...  {'id': None, 'name': 'Yahoo Entertainment'}\n",
+       "3  2025-04-10  AAPL crumble: stock hit again, as White House ...  After a brief respite on Wednesday, Apple's st...         {'id': None, 'name': 'AppleInsider'}\n",
+       "4  2025-04-28  Consumers get ahead of tariffs, Morgan Stanley...  Morgan Stanley hiked its Apple stock price tar...         {'id': None, 'name': 'AppleInsider'}"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
     }
    ],
    "source": [
@@ -297,7 +368,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 30,
    "id": "23508f73",
    "metadata": {},
    "outputs": [
@@ -305,7 +376,101 @@
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "Skipping sentiment analysis as no news articles were successfully fetched or the DataFrame is empty.\n"
+      "Performing sentiment analysis on 100 articles...\n",
+      "Sentiment analysis complete.\n",
+      "Sentiment analysis complete.\n"
+     ]
+    },
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>date</th>\n",
+       "      <th>title</th>\n",
+       "      <th>sentiment_label</th>\n",
+       "      <th>sentiment_score</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>2025-04-14</td>\n",
+       "      <td>AAPL opens market at highest price since Trump...</td>\n",
+       "      <td>positive</td>\n",
+       "      <td>0.926958</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>2025-04-03</td>\n",
+       "      <td>Apple stock drops almost 10% on market open in...</td>\n",
+       "      <td>negative</td>\n",
+       "      <td>0.974071</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>2025-04-25</td>\n",
+       "      <td>Mag 7 earnings, PCE data, April jobs report: W...</td>\n",
+       "      <td>neutral</td>\n",
+       "      <td>0.918775</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>2025-04-10</td>\n",
+       "      <td>AAPL crumble: stock hit again, as White House ...</td>\n",
+       "      <td>negative</td>\n",
+       "      <td>0.971886</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4</th>\n",
+       "      <td>2025-04-28</td>\n",
+       "      <td>Consumers get ahead of tariffs, Morgan Stanley...</td>\n",
+       "      <td>positive</td>\n",
+       "      <td>0.708044</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "         date                                              title sentiment_label  sentiment_score\n",
+       "0  2025-04-14  AAPL opens market at highest price since Trump...        positive         0.926958\n",
+       "1  2025-04-03  Apple stock drops almost 10% on market open in...        negative         0.974071\n",
+       "2  2025-04-25  Mag 7 earnings, PCE data, April jobs report: W...         neutral         0.918775\n",
+       "3  2025-04-10  AAPL crumble: stock hit again, as White House ...        negative         0.971886\n",
+       "4  2025-04-28  Consumers get ahead of tariffs, Morgan Stanley...        positive         0.708044"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "\n",
+      "Sentiment Label Distribution:\n",
+      "sentiment_label\n",
+      "negative    48\n",
+      "neutral     36\n",
+      "positive    16\n",
+      "Name: count, dtype: int64\n"
      ]
     }
    ],
@@ -332,6 +497,446 @@
     "else:\n",
     "    print(\"Skipping sentiment analysis as no news articles were successfully fetched or the DataFrame is empty.\")"
    ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "cde923dc",
+   "metadata": {},
+   "source": [
+    "## 5. Aggregate Sentiment and Merge Data\n",
+    "\n",
+    "Aggregate the daily sentiment scores and merge them with the stock price data."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 31,
+   "id": "1d9677e1",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Daily aggregated sentiment:\n"
+     ]
+    },
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>date</th>\n",
+       "      <th>avg_sentiment_score</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>2025-03-31</td>\n",
+       "      <td>0.889225</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>2025-04-01</td>\n",
+       "      <td>0.710846</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>2025-04-02</td>\n",
+       "      <td>0.846303</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>2025-04-03</td>\n",
+       "      <td>0.850788</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4</th>\n",
+       "      <td>2025-04-04</td>\n",
+       "      <td>0.833449</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "        date  avg_sentiment_score\n",
+       "0 2025-03-31             0.889225\n",
+       "1 2025-04-01             0.710846\n",
+       "2 2025-04-02             0.846303\n",
+       "3 2025-04-03             0.850788\n",
+       "4 2025-04-04             0.833449"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "\n",
+      "Merged Stock and Sentiment Data:\n"
+     ]
+    },
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>Date</th>\n",
+       "      <th>Open</th>\n",
+       "      <th>High</th>\n",
+       "      <th>Low</th>\n",
+       "      <th>Close</th>\n",
+       "      <th>Volume</th>\n",
+       "      <th>Dividends</th>\n",
+       "      <th>Stock Splits</th>\n",
+       "      <th>avg_sentiment_score</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>2025-03-31</td>\n",
+       "      <td>217.009995</td>\n",
+       "      <td>225.619995</td>\n",
+       "      <td>216.229996</td>\n",
+       "      <td>222.130005</td>\n",
+       "      <td>65299300</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.889225</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>2025-04-01</td>\n",
+       "      <td>219.809998</td>\n",
+       "      <td>223.679993</td>\n",
+       "      <td>218.899994</td>\n",
+       "      <td>223.190002</td>\n",
+       "      <td>36412700</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.710846</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>2025-04-02</td>\n",
+       "      <td>221.320007</td>\n",
+       "      <td>225.190002</td>\n",
+       "      <td>221.020004</td>\n",
+       "      <td>223.889999</td>\n",
+       "      <td>35905900</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.846303</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>2025-04-03</td>\n",
+       "      <td>205.539993</td>\n",
+       "      <td>207.490005</td>\n",
+       "      <td>201.250000</td>\n",
+       "      <td>203.190002</td>\n",
+       "      <td>103419000</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.850788</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4</th>\n",
+       "      <td>2025-04-04</td>\n",
+       "      <td>193.889999</td>\n",
+       "      <td>199.880005</td>\n",
+       "      <td>187.339996</td>\n",
+       "      <td>188.380005</td>\n",
+       "      <td>125910900</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.833449</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "        Date        Open        High         Low       Close     Volume  Dividends  Stock Splits  avg_sentiment_score\n",
+       "0 2025-03-31  217.009995  225.619995  216.229996  222.130005   65299300        0.0           0.0             0.889225\n",
+       "1 2025-04-01  219.809998  223.679993  218.899994  223.190002   36412700        0.0           0.0             0.710846\n",
+       "2 2025-04-02  221.320007  225.190002  221.020004  223.889999   35905900        0.0           0.0             0.846303\n",
+       "3 2025-04-03  205.539993  207.490005  201.250000  203.190002  103419000        0.0           0.0             0.850788\n",
+       "4 2025-04-04  193.889999  199.880005  187.339996  188.380005  125910900        0.0           0.0             0.833449"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "# Ensure 'date' column in news_df is datetime type if it exists\n",
+    "if 'news_df' in locals() and not news_df.empty and 'date' in news_df.columns:\n",
+    "    news_df['date'] = pd.to_datetime(news_df['date'])\n",
+    "    # Aggregate sentiment: Calculate the mean sentiment score per day\n",
+    "    # Filter out rows where sentiment_score might be None or NaN before aggregation\n",
+    "    valid_sentiment_df = news_df.dropna(subset=['sentiment_score'])\n",
+    "    daily_sentiment = valid_sentiment_df.groupby('date')['sentiment_score'].mean().reset_index()\n",
+    "    daily_sentiment.rename(columns={'sentiment_score': 'avg_sentiment_score'}, inplace=True)\n",
+    "    print(\"Daily aggregated sentiment:\")\n",
+    "    display(daily_sentiment.head())\n",
+    "\n",
+    "    # Ensure 'Date' column in stock_df is datetime type\n",
+    "    if 'stock_df' in locals() and stock_df is not None:\n",
+    "        stock_df['Date'] = pd.to_datetime(stock_df['Date'])\n",
+    "        # Merge stock data with aggregated sentiment data\n",
+    "        merged_df = pd.merge(stock_df, daily_sentiment, left_on='Date', right_on='date', how='left')\n",
+    "        # Drop the redundant 'date' column from the sentiment df\n",
+    "        if 'date' in merged_df.columns:\n",
+    "             merged_df.drop(columns=['date'], inplace=True)\n",
+    "        print(\"\\nMerged Stock and Sentiment Data:\")\n",
+    "        display(merged_df.head())\n",
+    "    else:\n",
+    "        print(\"Stock data (stock_df) not available for merging.\")\n",
+    "        merged_df = None # Set merged_df to None if stock data is missing\n",
+    "else:\n",
+    "    print(\"News data with sentiment (news_df) not available or empty. Cannot aggregate or merge.\")\n",
+    "    merged_df = None # Set merged_df to None if news data is missing"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "f4c89e52",
+   "metadata": {},
+   "source": [
+    "## 6. Visualize Data\n",
+    "\n",
+    "Plot the stock closing price and the aggregated daily sentiment score over time."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 32,
+   "id": "db40d1f7",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": "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",
+      "text/plain": [
+       "<Figure size 1400x700 with 2 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "import matplotlib.pyplot as plt\n",
+    "import matplotlib.dates as mdates\n",
+    "\n",
+    "if merged_df is not None and not merged_df.empty:\n",
+    "    fig, ax1 = plt.subplots(figsize=(14, 7))\n",
+    "\n",
+    "    # Plot Stock Price\n",
+    "    color = 'tab:blue'\n",
+    "    ax1.set_xlabel('Date')\n",
+    "    ax1.set_ylabel('Stock Close Price', color=color)\n",
+    "    ax1.plot(merged_df['Date'], merged_df['Close'], color=color, label='Stock Price')\n",
+    "    ax1.tick_params(axis='y', labelcolor=color)\n",
+    "\n",
+    "    # Create a second y-axis for the sentiment score\n",
+    "    ax2 = ax1.twinx()\n",
+    "    color = 'tab:red'\n",
+    "    ax2.set_ylabel('Average Sentiment Score', color=color)\n",
+    "    # Plot Sentiment Score - use points or dashed line to differentiate\n",
+    "    ax2.plot(merged_df['Date'], merged_df['avg_sentiment_score'], color=color, linestyle='--', marker='o', markersize=4, label='Avg Sentiment')\n",
+    "    ax2.tick_params(axis='y', labelcolor=color)\n",
+    "    # Add a horizontal line at sentiment score 0 for reference\n",
+    "    ax2.axhline(0, color='grey', linestyle='--', linewidth=0.8)\n",
+    "\n",
+    "    # Formatting\n",
+    "    fig.tight_layout() # Adjust layout to prevent overlap\n",
+    "    plt.title(f'{TICKER} Stock Price vs. Average Daily News Sentiment')\n",
+    "    ax1.xaxis.set_major_locator(mdates.WeekdayLocator(interval=1)) # Show weekly ticks\n",
+    "    ax1.xaxis.set_major_formatter(mdates.DateFormatter('%Y-%m-%d'))\n",
+    "    plt.xticks(rotation=45)\n",
+    "    # Add legends\n",
+    "    lines, labels = ax1.get_legend_handles_labels()\n",
+    "    lines2, labels2 = ax2.get_legend_handles_labels()\n",
+    "    ax2.legend(lines + lines2, labels + labels2, loc='upper left')\n",
+    "\n",
+    "    plt.grid(True, which='major', linestyle='--', linewidth='0.5', color='grey')\n",
+    "    plt.show()\n",
+    "else:\n",
+    "    print(\"Merged data not available or empty. Cannot create visualization.\")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "e05ff402",
+   "metadata": {},
+   "source": [
+    "## 7. Correlation Analysis\n",
+    "\n",
+    "Calculate the correlation between daily sentiment scores and stock price changes.\n",
+    "We'll look at the correlation between sentiment on day D and price change from day D to day D+1."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 33,
+   "id": "f1b70cca",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Correlation between lagged sentiment score and daily price change: -0.0235\n"
+     ]
+    },
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>Date</th>\n",
+       "      <th>Close</th>\n",
+       "      <th>price_pct_change</th>\n",
+       "      <th>avg_sentiment_score</th>\n",
+       "      <th>sentiment_lagged</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>2025-03-31</td>\n",
+       "      <td>222.130005</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>0.889225</td>\n",
+       "      <td>NaN</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>2025-04-01</td>\n",
+       "      <td>223.190002</td>\n",
+       "      <td>0.004772</td>\n",
+       "      <td>0.710846</td>\n",
+       "      <td>0.889225</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>2025-04-02</td>\n",
+       "      <td>223.889999</td>\n",
+       "      <td>0.003136</td>\n",
+       "      <td>0.846303</td>\n",
+       "      <td>0.710846</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>2025-04-03</td>\n",
+       "      <td>203.190002</td>\n",
+       "      <td>-0.092456</td>\n",
+       "      <td>0.850788</td>\n",
+       "      <td>0.846303</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4</th>\n",
+       "      <td>2025-04-04</td>\n",
+       "      <td>188.380005</td>\n",
+       "      <td>-0.072887</td>\n",
+       "      <td>0.833449</td>\n",
+       "      <td>0.850788</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "        Date       Close  price_pct_change  avg_sentiment_score  sentiment_lagged\n",
+       "0 2025-03-31  222.130005               NaN             0.889225               NaN\n",
+       "1 2025-04-01  223.190002          0.004772             0.710846          0.889225\n",
+       "2 2025-04-02  223.889999          0.003136             0.846303          0.710846\n",
+       "3 2025-04-03  203.190002         -0.092456             0.850788          0.846303\n",
+       "4 2025-04-04  188.380005         -0.072887             0.833449          0.850788"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "if merged_df is not None and not merged_df.empty and 'avg_sentiment_score' in merged_df.columns:\n",
+    "    # Calculate daily percentage change in closing price\n",
+    "    merged_df['price_pct_change'] = merged_df['Close'].pct_change()\n",
+    "\n",
+    "    # Shift sentiment scores to align sentiment from day D with price change from D to D+1\n",
+    "    merged_df['sentiment_lagged'] = merged_df['avg_sentiment_score'].shift(1)\n",
+    "\n",
+    "    # Calculate correlation between lagged sentiment and price change\n",
+    "    # Drop NaN values that result from pct_change and shift\n",
+    "    correlation_df = merged_df[['sentiment_lagged', 'price_pct_change']].dropna()\n",
+    "\n",
+    "    if not correlation_df.empty:\n",
+    "        correlation = correlation_df['sentiment_lagged'].corr(correlation_df['price_pct_change'])\n",
+    "        print(f\"Correlation between lagged sentiment score and daily price change: {correlation:.4f}\")\n",
+    "\n",
+    "        # Display the relevant columns for inspection\n",
+    "        display(merged_df[['Date', 'Close', 'price_pct_change', 'avg_sentiment_score', 'sentiment_lagged']].head())\n",
+    "    else:\n",
+    "        print(\"Not enough overlapping data points to calculate correlation after lagging and calculating price change.\")\n",
+    "else:\n",
+    "    print(\"Merged data not available, empty, or missing 'avg_sentiment_score'. Cannot calculate correlation.\")"
+   ]
   }
  ],
  "metadata": {