Diwali_Sales_Analysis.ipynb

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    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Collecting seaborn\n",
      "  Downloading seaborn-0.13.2-py3-none-any.whl.metadata (5.4 kB)\n",
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      "Downloading seaborn-0.13.2-py3-none-any.whl (294 kB)\n",
      "Installing collected packages: seaborn\n",
      "Successfully installed seaborn-0.13.2\n"
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   ],
   "source": [
    "!pip install seaborn\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "0979edbb",
   "metadata": {},
   "outputs": [],
   "source": [
    "# import python libraries\n",
    "\n",
    "import numpy as np \n",
    "import pandas as pd \n",
    "import matplotlib.pyplot as plt # visualizing data\n",
    "%matplotlib inline\n",
    "import seaborn as sns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "7fbfb436",
   "metadata": {},
   "outputs": [],
   "source": [
    "# import csv file\n",
    "df = pd.read_csv('Diwali Sales Data.csv', encoding= 'unicode_escape')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "b8db4a5b",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(11251, 15)"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "id": "662c471c",
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "<div>\n",
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       "    .dataframe tbody tr th:only-of-type {\n",
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       "    }\n",
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       "    }\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>User_ID</th>\n",
       "      <th>Cust_name</th>\n",
       "      <th>Product_ID</th>\n",
       "      <th>Gender</th>\n",
       "      <th>Age Group</th>\n",
       "      <th>Age</th>\n",
       "      <th>Marital_Status</th>\n",
       "      <th>State</th>\n",
       "      <th>Zone</th>\n",
       "      <th>Occupation</th>\n",
       "      <th>Product_Category</th>\n",
       "      <th>Orders</th>\n",
       "      <th>Amount</th>\n",
       "      <th>Status</th>\n",
       "      <th>unnamed1</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1002903</td>\n",
       "      <td>Sanskriti</td>\n",
       "      <td>P00125942</td>\n",
       "      <td>F</td>\n",
       "      <td>26-35</td>\n",
       "      <td>28</td>\n",
       "      <td>0</td>\n",
       "      <td>Maharashtra</td>\n",
       "      <td>Western</td>\n",
       "      <td>Healthcare</td>\n",
       "      <td>Auto</td>\n",
       "      <td>1</td>\n",
       "      <td>23952.0</td>\n",
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       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1000732</td>\n",
       "      <td>Kartik</td>\n",
       "      <td>P00110942</td>\n",
       "      <td>F</td>\n",
       "      <td>26-35</td>\n",
       "      <td>35</td>\n",
       "      <td>1</td>\n",
       "      <td>Andhra Pradesh</td>\n",
       "      <td>Southern</td>\n",
       "      <td>Govt</td>\n",
       "      <td>Auto</td>\n",
       "      <td>3</td>\n",
       "      <td>23934.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>1001990</td>\n",
       "      <td>Bindu</td>\n",
       "      <td>P00118542</td>\n",
       "      <td>F</td>\n",
       "      <td>26-35</td>\n",
       "      <td>35</td>\n",
       "      <td>1</td>\n",
       "      <td>Uttar Pradesh</td>\n",
       "      <td>Central</td>\n",
       "      <td>Automobile</td>\n",
       "      <td>Auto</td>\n",
       "      <td>3</td>\n",
       "      <td>23924.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
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       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1001425</td>\n",
       "      <td>Sudevi</td>\n",
       "      <td>P00237842</td>\n",
       "      <td>M</td>\n",
       "      <td>0-17</td>\n",
       "      <td>16</td>\n",
       "      <td>0</td>\n",
       "      <td>Karnataka</td>\n",
       "      <td>Southern</td>\n",
       "      <td>Construction</td>\n",
       "      <td>Auto</td>\n",
       "      <td>2</td>\n",
       "      <td>23912.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>1000588</td>\n",
       "      <td>Joni</td>\n",
       "      <td>P00057942</td>\n",
       "      <td>M</td>\n",
       "      <td>26-35</td>\n",
       "      <td>28</td>\n",
       "      <td>1</td>\n",
       "      <td>Gujarat</td>\n",
       "      <td>Western</td>\n",
       "      <td>Food Processing</td>\n",
       "      <td>Auto</td>\n",
       "      <td>2</td>\n",
       "      <td>23877.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
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      "text/plain": [
       "   User_ID  Cust_name Product_ID Gender Age Group  Age  Marital_Status  \\\n",
       "0  1002903  Sanskriti  P00125942      F     26-35   28               0   \n",
       "1  1000732     Kartik  P00110942      F     26-35   35               1   \n",
       "2  1001990      Bindu  P00118542      F     26-35   35               1   \n",
       "3  1001425     Sudevi  P00237842      M      0-17   16               0   \n",
       "4  1000588       Joni  P00057942      M     26-35   28               1   \n",
       "\n",
       "            State      Zone       Occupation Product_Category  Orders  \\\n",
       "0     Maharashtra   Western       Healthcare             Auto       1   \n",
       "1  Andhra Pradesh  Southern             Govt             Auto       3   \n",
       "2   Uttar Pradesh   Central       Automobile             Auto       3   \n",
       "3       Karnataka  Southern     Construction             Auto       2   \n",
       "4         Gujarat   Western  Food Processing             Auto       2   \n",
       "\n",
       "    Amount  Status  unnamed1  \n",
       "0  23952.0     NaN       NaN  \n",
       "1  23934.0     NaN       NaN  \n",
       "2  23924.0     NaN       NaN  \n",
       "3  23912.0     NaN       NaN  \n",
       "4  23877.0     NaN       NaN  "
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "id": "0814c45b",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 11251 entries, 0 to 11250\n",
      "Data columns (total 15 columns):\n",
      " #   Column            Non-Null Count  Dtype  \n",
      "---  ------            --------------  -----  \n",
      " 0   User_ID           11251 non-null  int64  \n",
      " 1   Cust_name         11251 non-null  object \n",
      " 2   Product_ID        11251 non-null  object \n",
      " 3   Gender            11251 non-null  object \n",
      " 4   Age Group         11251 non-null  object \n",
      " 5   Age               11251 non-null  int64  \n",
      " 6   Marital_Status    11251 non-null  int64  \n",
      " 7   State             11251 non-null  object \n",
      " 8   Zone              11251 non-null  object \n",
      " 9   Occupation        11251 non-null  object \n",
      " 10  Product_Category  11251 non-null  object \n",
      " 11  Orders            11251 non-null  int64  \n",
      " 12  Amount            11239 non-null  float64\n",
      " 13  Status            0 non-null      float64\n",
      " 14  unnamed1          0 non-null      float64\n",
      "dtypes: float64(3), int64(4), object(8)\n",
      "memory usage: 1.3+ MB\n"
     ]
    }
   ],
   "source": [
    "df.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "id": "29afa95b",
   "metadata": {},
   "outputs": [],
   "source": [
    "#drop unrelated/blank columns\n",
    "df.drop(['Status', 'unnamed1'], axis=1, inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "5cee58eb",
   "metadata": {},
   "outputs": [],
   "source": [
    "#check for null values\n",
    "pd.isnull(df).sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "65de74e3",
   "metadata": {},
   "outputs": [],
   "source": [
    "# drop null values\n",
    "df.dropna(inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "14e4ff48",
   "metadata": {},
   "outputs": [],
   "source": [
    "# change data type\n",
    "df['Amount'] = df['Amount'].astype('int')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "aa6752dc",
   "metadata": {},
   "outputs": [],
   "source": [
    "df['Amount'].dtypes"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "5f723a99",
   "metadata": {},
   "outputs": [],
   "source": [
    "df.columns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "a85bb3c3",
   "metadata": {},
   "outputs": [],
   "source": [
    "#rename column\n",
    "df.rename(columns= {'Marital_Status':'Shaadi'})"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "a2484863",
   "metadata": {},
   "outputs": [],
   "source": [
    "# describe() method returns description of the data in the DataFrame (i.e. count, mean, std, etc)\n",
    "df.describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "2c380030",
   "metadata": {},
   "outputs": [],
   "source": [
    "# use describe() for specific columns\n",
    "df[['Age', 'Orders', 'Amount']].describe()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "cc649558",
   "metadata": {},
   "source": [
    "# Exploratory Data Analysis"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "504b419c",
   "metadata": {},
   "source": [
    "### Gender"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "84c7918d",
   "metadata": {},
   "outputs": [],
   "source": [
    "# plotting a bar chart for Gender and it's count\n",
    "\n",
    "ax = sns.countplot(x = 'Gender',data = df)\n",
    "\n",
    "for bars in ax.containers:\n",
    "    ax.bar_label(bars)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "c5fd4566",
   "metadata": {},
   "outputs": [],
   "source": [
    "# plotting a bar chart for gender vs total amount\n",
    "\n",
    "sales_gen = df.groupby(['Gender'], as_index=False)['Amount'].sum().sort_values(by='Amount', ascending=False)\n",
    "\n",
    "sns.barplot(x = 'Gender',y= 'Amount' ,data = sales_gen)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "1e108cec",
   "metadata": {},
   "source": [
    "*From above graphs we can see that most of the buyers are females and even the purchasing power of females are greater than men*"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "dcc927f9",
   "metadata": {},
   "source": [
    "### Age"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "88563dec",
   "metadata": {},
   "outputs": [],
   "source": [
    "ax = sns.countplot(data = df, x = 'Age Group', hue = 'Gender')\n",
    "\n",
    "for bars in ax.containers:\n",
    "    ax.bar_label(bars)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "bfbe3937",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Total Amount vs Age Group\n",
    "sales_age = df.groupby(['Age Group'], as_index=False)['Amount'].sum().sort_values(by='Amount', ascending=False)\n",
    "\n",
    "sns.barplot(x = 'Age Group',y= 'Amount' ,data = sales_age)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "abaa6c07",
   "metadata": {},
   "source": [
    "*From above graphs we can see that most of the buyers are of age group between 26-35 yrs female*"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "83a49098",
   "metadata": {},
   "source": [
    "### State"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "dfac4f2c",
   "metadata": {},
   "outputs": [],
   "source": [
    "# total number of orders from top 10 states\n",
    "\n",
    "sales_state = df.groupby(['State'], as_index=False)['Orders'].sum().sort_values(by='Orders', ascending=False).head(10)\n",
    "\n",
    "sns.set(rc={'figure.figsize':(15,5)})\n",
    "sns.barplot(data = sales_state, x = 'State',y= 'Orders')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "a66617bc",
   "metadata": {},
   "outputs": [],
   "source": [
    "# total amount/sales from top 10 states\n",
    "\n",
    "sales_state = df.groupby(['State'], as_index=False)['Amount'].sum().sort_values(by='Amount', ascending=False).head(10)\n",
    "\n",
    "sns.set(rc={'figure.figsize':(15,5)})\n",
    "sns.barplot(data = sales_state, x = 'State',y= 'Amount')"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "39d46e9e",
   "metadata": {},
   "source": [
    "*From above graphs we can see that most of the orders & total sales/amount are from Uttar Pradesh, Maharashtra and Karnataka respectively*\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "89f865a5",
   "metadata": {},
   "source": [
    "### Marital Status"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "77e6635e",
   "metadata": {},
   "outputs": [],
   "source": [
    "ax = sns.countplot(data = df, x = 'Marital_Status')\n",
    "\n",
    "sns.set(rc={'figure.figsize':(7,5)})\n",
    "for bars in ax.containers:\n",
    "    ax.bar_label(bars)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "2d8c7b27",
   "metadata": {},
   "outputs": [],
   "source": [
    "sales_state = df.groupby(['Marital_Status', 'Gender'], as_index=False)['Amount'].sum().sort_values(by='Amount', ascending=False)\n",
    "\n",
    "sns.set(rc={'figure.figsize':(6,5)})\n",
    "sns.barplot(data = sales_state, x = 'Marital_Status',y= 'Amount', hue='Gender')"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "37afbb83",
   "metadata": {},
   "source": [
    "*From above graphs we can see that most of the buyers are married (women) and they have high purchasing power*"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "230cfe66",
   "metadata": {},
   "source": [
    "### Occupation"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "id": "4deb41f4",
   "metadata": {},
   "outputs": [
    {
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",
      "text/plain": [
       "<Figure size 2000x500 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.set(rc={'figure.figsize':(20,5)})\n",
    "ax = sns.countplot(data = df, x = 'Occupation')\n",
    "\n",
    "for bars in ax.containers:\n",
    "    ax.bar_label(bars)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "id": "4eb620f6",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<Axes: xlabel='Occupation', ylabel='Amount'>"
      ]
     },
     "execution_count": 24,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<Figure size 2000x500 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sales_state = df.groupby(['Occupation'], as_index=False)['Amount'].sum().sort_values(by='Amount', ascending=False)\n",
    "\n",
    "sns.set(rc={'figure.figsize':(20,5)})\n",
    "sns.barplot(data = sales_state, x = 'Occupation',y= 'Amount')"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "26c22532",
   "metadata": {},
   "source": [
    "*From above graphs we can see that most of the buyers are working in IT, Healthcare and Aviation sector*"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "40518579",
   "metadata": {},
   "source": [
    "### Product Category"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "id": "3e7e4d39",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 2000x500 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.set(rc={'figure.figsize':(20,5)})\n",
    "ax = sns.countplot(data = df, x = 'Product_Category')\n",
    "\n",
    "for bars in ax.containers:\n",
    "    ax.bar_label(bars)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "id": "99f5c6f3",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<Axes: xlabel='Product_Category', ylabel='Amount'>"
      ]
     },
     "execution_count": 26,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 2000x500 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sales_state = df.groupby(['Product_Category'], as_index=False)['Amount'].sum().sort_values(by='Amount', ascending=False).head(10)\n",
    "\n",
    "sns.set(rc={'figure.figsize':(20,5)})\n",
    "sns.barplot(data = sales_state, x = 'Product_Category',y= 'Amount')"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "5be54973",
   "metadata": {},
   "source": [
    "*From above graphs we can see that most of the sold products are from Food, Clothing and Electronics category*"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "id": "f33d23c7",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<Axes: xlabel='Product_ID', ylabel='Orders'>"
      ]
     },
     "execution_count": 27,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 2000x500 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sales_state = df.groupby(['Product_ID'], as_index=False)['Orders'].sum().sort_values(by='Orders', ascending=False).head(10)\n",
    "\n",
    "sns.set(rc={'figure.figsize':(20,5)})\n",
    "sns.barplot(data = sales_state, x = 'Product_ID',y= 'Orders')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "id": "17d2bb43",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<Axes: xlabel='Product_ID'>"
      ]
     },
     "execution_count": 28,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1200x700 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# top 10 most sold products (same thing as above)\n",
    "\n",
    "fig1, ax1 = plt.subplots(figsize=(12,7))\n",
    "df.groupby('Product_ID')['Orders'].sum().nlargest(10).sort_values(ascending=False).plot(kind='bar')"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "31f88f63",
   "metadata": {},
   "source": [
    "## Conclusion:\n",
    "\n",
    "### "
   ]
  },
  {
   "cell_type": "markdown",
   "id": "56e52bca",
   "metadata": {},
   "source": [
    "*Married women age group 26-35 yrs from UP,  Maharastra and Karnataka working in IT, Healthcare and Aviation are more likely to buy products from Food, Clothing and Electronics category*"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3 (ipykernel)",
   "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": "3.12.5"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 5
}