Yet_another_copy_of_SentimentAnalysis.ipynb

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          "output_type": "error",
          "ename": "IndentationError",
          "evalue": "unexpected indent (<ipython-input-3-cbcedf656fe3>, line 126)",
          "traceback": [
            "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
            "\u001b[0;31mIndentationError\u001b[0m                          Traceback (most recent call last)",
            "\u001b[0;32m/usr/local/lib/python3.10/dist-packages/IPython/core/compilerop.py\u001b[0m in \u001b[0;36mast_parse\u001b[0;34m(self, source, filename, symbol)\u001b[0m\n\u001b[1;32m     99\u001b[0m         \u001b[0mArguments\u001b[0m \u001b[0mare\u001b[0m \u001b[0mexactly\u001b[0m \u001b[0mthe\u001b[0m \u001b[0msame\u001b[0m \u001b[0;32mas\u001b[0m \u001b[0mast\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mparse\u001b[0m \u001b[0;34m(\u001b[0m\u001b[0;32min\u001b[0m \u001b[0mthe\u001b[0m \u001b[0mstandard\u001b[0m \u001b[0mlibrary\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    100\u001b[0m         and are passed to the built-in compile function.\"\"\"\n\u001b[0;32m--> 101\u001b[0;31m         \u001b[0;32mreturn\u001b[0m \u001b[0mcompile\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0msource\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mfilename\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0msymbol\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mflags\u001b[0m \u001b[0;34m|\u001b[0m \u001b[0mPyCF_ONLY_AST\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;36m1\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    102\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    103\u001b[0m     \u001b[0;32mdef\u001b[0m \u001b[0mreset_compiler_flags\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;31mIndentationError\u001b[0m: unexpected indent (<ipython-input-3-cbcedf656fe3>, line 126)"
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      "source": [
        "\n",
        "!pip install numpy\n",
        "import numpy as np\n",
        "print(np.__version__)\n",
        "\n",
        "!pip install pandas\n",
        "import pandas as pd\n",
        "print(pd.__version__)\n",
        "!pip install nltk\n",
        "import nltk\n",
        "print(nltk.__version__)\n",
        "from nltk.sentiment.vader import SentimentIntensityAnalyzer\n",
        "import re\n",
        "from textblob import TextBlob\n",
        "from wordcloud import WordCloud\n",
        "import seaborn as sns\n",
        "import matplotlib.pyplot as plt\n",
        "import cufflinks as cf\n",
        "%matplotlib inline\n",
        "from plotly.offline import init_notebook_mode,iplot\n",
        "init_notebook_mode(connected = True)\n",
        "cf.go_offline();\n",
        "import plotly.graph_objs as go\n",
        "from plotly.subplots import make_subplots\n",
        "\n",
        "import warnings\n",
        "warnings.filterwarnings(\"ignore\")\n",
        "warnings.warn(\"this will not show\")\n",
        "pd.set_option('display.max_columns',None)\n",
        "df= pd.read_csv(\"/content/amazon.csv\")\n",
        "del df[df.columns[0]]\n",
        "import pandas as pd\n",
        "\n",
        "# Load your dataset\n",
        "df = pd.read_csv(\"/content/amazon.csv\")\n",
        "columns_to_clean=['reviewerName','overall','reviewText','reviewTime','day_diff','helpful_yes','helpful_no','total_vote','score_pos_neg_diff','score_average_rating','wilson_lower_bound']\n",
        "df_cleaned=df.dropna(subset=columns_to_clean)\n",
        "df_cleaned.to_csv('cleaned_dataset.csv',index=False)\n",
        "\n",
        "# Check for missing values\n",
        "df = pd.read_csv(\"/content/cleaned_dataset.csv\")\n",
        "missing_values = df.isnull().sum().sum()\n",
        "if missing_values == 0:\n",
        "    print(\"No missing values found.\")\n",
        "else:\n",
        "    print(\"Missing values found in the dataset.total:{missing_values}\")\n",
        "\n",
        "# Check for duplicates\n",
        "duplicate_rows = df.duplicated().sum()\n",
        "if duplicate_rows == 0:\n",
        "    print(\"No duplicate rows found.\")\n",
        "else:\n",
        "    print(\"Duplicate rows found.\")\n",
        "\n",
        "# Validate data types\n",
        "print(\"Data types:\")\n",
        "print(df.dtypes)\n",
        "\n",
        "# Explore outliers (This example assumes numerical columns)\n",
        "outliers = df.describe().loc[['min', 'max']].T\n",
        "print(\"Outliers (min/max values per column):\")\n",
        "print(outliers)\n",
        "df = pd.read_csv(\"/content/cleaned_dataset.csv\")\n",
        "def missing_values_analysis(df):\n",
        "  na_columns_=[col for col in df.columns if df[col].isnull().sum()>0]\n",
        "  n_miss = pd.Series(df[na_columns_].isnull().sum())\n",
        "  ratio_ = (df[na_columns_].isnull().sum()/df.shape[0]*100).sort_values(ascending=True)\n",
        "  missing_df = pd.concat([n_miss,np.round(ratio_,2)],axis=1,keys=['Missing Values','Ratio'])\n",
        "  return missing_df\n",
        "def check_dataframe(df, head=5, tail=5):\n",
        "    print(\"SHAPE\".center(82, '~'))\n",
        "    print('Rows: {}'.format(df.shape[0]))\n",
        "    print('Columns: {}'.format(df.shape[1]))\n",
        "    print(\"TYPES\".center(82, '~'))\n",
        "    print(df.dtypes)\n",
        "    print(\"\".center(82, '~'))\n",
        "    print(missing_values_analysis(df))\n",
        "    print('DUPLICATED VALUES'.center(83, '~'))\n",
        "    print(df.duplicated().sum())\n",
        "    print(\"QUANTILES\".center(82, '~'))\n",
        "    numerical_columns=df.select_dtypes(include=['int','float'])\n",
        "    quantiles=numerical_columns.quantile([0,0.05,0.50,0.95,0.99,1])\n",
        "    print(\"QUANTILES:\")\n",
        "    print(quantiles)\n",
        "check_dataframe(df)\n",
        "def check_class(data_frame):\n",
        "  nunique_df=pd.DataFrame({'Variable':data_frame.columns,\n",
        "                           'Classes':[data_frame[i].nunique()\\\n",
        "                                      for i in data_frame.columns]})\n",
        "  nunique_df=nunique_df.sort_values('Classes',ascending=False)\n",
        "  nunique_df=nunique_df.reset_index(drop=True)\n",
        "  return nunique_df\n",
        "check_class(df)\n",
        "\n",
        "import plotly.graph_objects as go\n",
        "from plotly.subplots import make_subplots\n",
        "constraints=['#B34D22','#EBE00C','#1FEB0C','#0C92EB','#EB0CD5']\n",
        "def categeorical_variable_summary(df,column_name):\n",
        "  fig=make_subplots(rows=1,cols=2,\n",
        "                    subplot_titles=('Countplot','Percentage'),\n",
        "                    specs=[[{\"type\":\"xy\"},{'type': \"domain\"}]]) # Define domain here\n",
        "  fig.add_trace(go.Bar(y=df[column_name].value_counts().values.tolist(),\n",
        "                       x=[str(i)for i in df[column_name].value_counts().index],\n",
        "                       text=df[column_name].value_counts().values.tolist(),\n",
        "                       textfont=dict(size=14),\n",
        "                       name=column_name,\n",
        "                       textposition='auto',\n",
        "                      showlegend=False,\n",
        "                       marker=dict(color=constraints,\n",
        "                                   line=dict(color='#DBE6EC',\n",
        "                                             width=1))),\n",
        "                              row=1,col=1)\n",
        "  fig.add_trace(go.Pie(labels=df[column_name].value_counts().keys(),\n",
        "                       values=df[column_name].value_counts().values,\n",
        "                       textfont=dict(size=18),\n",
        "                       textposition='auto',\n",
        "                       showlegend=False,\n",
        "                       name=column_name,\n",
        "                       marker=dict(colors=constraints)),\n",
        "                row=1,col=2)\n",
        "  fig.update_layout(title={'text':column_name,\n",
        "                           'y':0.9,\n",
        "                           'x':0.5,\n",
        "                           'xanchor':'center',\n",
        "                           'yanchor':'top'},\n",
        "                    template='plotly_white')\n",
        "    display(fig)\n",
        "\n",
        "categeorical_variable_summary(df,'overall')\n",
        "\n",
        "\n",
        "\n",
        "\n",
        "\n",
        "\n",
        "\n",
        "\n",
        "\n",
        "\n",
        "\n",
        "\n",
        "\n"
      ]
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        "# New Section"
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      "cell_type": "code",
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