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Data-Analyst-Course

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Data-Analyst-Course
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{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 25,
   "id": "10d8d873",
   "metadata": {},
   "outputs": [],
   "source": [
    "# !pip install openpyxl\n",
    "# ! pip install pandas"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "id": "850ccb97",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "                                             Comment   Comment_Type  \\\n",
      "0  খুবই আরাম দায়ক এবং শীতের জন্য ঘরে পরার পারফেক্...       Positive   \n",
      "1  স্লিপার গুলা দাম হিসেবে অনেকভালো।\\nধন্যবাদ সেল...  Very Positive   \n",
      "2  প্রাইজ হিসেবে মান ঠিক আছে। রেগুলার ইউজ করার সু...       Positive   \n",
      "3  দাম অনুসারে ভালোই।আমি ১১০ টাকা দিয়ে কিনেছি।যদি...       Positive   \n",
      "4  আলহামদুলিল্লাহ জুতার মান ভালো। কালার ছবির মতোই...       Positive   \n",
      "\n",
      "  Product_Type  \n",
      "0      Fashion  \n",
      "1      Fashion  \n",
      "2      Fashion  \n",
      "3      Fashion  \n",
      "4      Fashion  \n"
     ]
    }
   ],
   "source": [
    "# Load the Excel file using pandas and the openpyxl engine. Display the first 5 rows.\n",
    "import pandas as pd\n",
    "from openpyxl import load_workbook\n",
    "\n",
    "df = pd.read_excel('data_Multi.xlsx')\n",
    "print(df.head())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "id": "62393b93",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Comment_Type\n",
      "Very Positive    285\n",
      "Very Negative    249\n",
      "Negative         238\n",
      "Positive         228\n",
      "Name: count, dtype: int64\n"
     ]
    }
   ],
   "source": [
    "# How many reviews fall under each sentiment (Comment_Type)?\n",
    "print(df['Comment_Type'].value_counts())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "id": "a8f6b45d",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Product_Type\n",
      "Electronics        202\n",
      "Household Stuff    202\n",
      "Beauty Products    200\n",
      "Fashion            198\n",
      "Clothing           198\n",
      "Name: count, dtype: int64\n"
     ]
    }
   ],
   "source": [
    "# How many reviews are there for each Product_Type?\n",
    "print(df['Product_Type'].value_counts())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "id": "b6c4869e",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
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      "findfont: Font family 'Noto Sans Bengali' not found.\n",
      "findfont: Font family 'Noto Sans Bengali' not found.\n"
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    {
     "data": {
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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Create a bar chart showing the number of reviews for each sentiment type.\n",
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sns\n",
    "\n",
    "sns.countplot(data=df, x='Comment_Type')\n",
    "plt.title('Sentiment Distribution')\n",
    "plt.xlabel('Comment Type')\n",
    "plt.ylabel('Count')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "id": "b162d0fc",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[('ভালো', 284), ('অনেক', 220), ('একটা', 140), ('খুবই', 129), ('টা', 125), ('সুন্দর', 114), ('না', 111), ('খুব', 109), ('দাম', 101), ('আর', 99)]\n"
     ]
    }
   ],
   "source": [
    "# What are the 10 most common words in the Comment column (simple whitespace split)?\n",
    "from collections import Counter\n",
    "\n",
    "all_words = ' '.join(df['Comment']).split()\n",
    "word_freq = Counter(all_words).most_common(10)\n",
    "print(word_freq)\n"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "all",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.12.9"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 5
}