Analyzing_resumes_in_pdf_format.ipynb

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      "provenance": []
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    "kernelspec": {
      "name": "python3",
      "display_name": "Python 3"
    },
    "language_info": {
      "name": "python"
    }
  },
  "cells": [
    {
      "cell_type": "code",
      "source": [
        "# Install necessary packages"
      ],
      "metadata": {
        "id": "zpdOAAow9uPT"
      },
      "execution_count": 2,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "!pip install PyPDF2 textblob nltk matplotlib  # Install required packages for PDF reading, text analysis, and visualization\n"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "lSuzSm1c9uRs",
        "outputId": "1f9050c5-1fdd-4ee2-e1f1-ea8f3a67319b"
      },
      "execution_count": 3,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Requirement already satisfied: PyPDF2 in /usr/local/lib/python3.10/dist-packages (3.0.1)\n",
            "Requirement already satisfied: textblob in /usr/local/lib/python3.10/dist-packages (0.17.1)\n",
            "Requirement already satisfied: nltk in /usr/local/lib/python3.10/dist-packages (3.8.1)\n",
            "Requirement already satisfied: matplotlib in /usr/local/lib/python3.10/dist-packages (3.7.1)\n",
            "Requirement already satisfied: click in /usr/local/lib/python3.10/dist-packages (from nltk) (8.1.7)\n",
            "Requirement already satisfied: joblib in /usr/local/lib/python3.10/dist-packages (from nltk) (1.4.2)\n",
            "Requirement already satisfied: regex>=2021.8.3 in /usr/local/lib/python3.10/dist-packages (from nltk) (2024.5.15)\n",
            "Requirement already satisfied: tqdm in /usr/local/lib/python3.10/dist-packages (from nltk) (4.66.4)\n",
            "Requirement already satisfied: contourpy>=1.0.1 in /usr/local/lib/python3.10/dist-packages (from matplotlib) (1.2.1)\n",
            "Requirement already satisfied: cycler>=0.10 in /usr/local/lib/python3.10/dist-packages (from matplotlib) (0.12.1)\n",
            "Requirement already satisfied: fonttools>=4.22.0 in /usr/local/lib/python3.10/dist-packages (from matplotlib) (4.53.1)\n",
            "Requirement already satisfied: kiwisolver>=1.0.1 in /usr/local/lib/python3.10/dist-packages (from matplotlib) (1.4.5)\n",
            "Requirement already satisfied: numpy>=1.20 in /usr/local/lib/python3.10/dist-packages (from matplotlib) (1.26.4)\n",
            "Requirement already satisfied: packaging>=20.0 in /usr/local/lib/python3.10/dist-packages (from matplotlib) (24.1)\n",
            "Requirement already satisfied: pillow>=6.2.0 in /usr/local/lib/python3.10/dist-packages (from matplotlib) (9.4.0)\n",
            "Requirement already satisfied: pyparsing>=2.3.1 in /usr/local/lib/python3.10/dist-packages (from matplotlib) (3.1.2)\n",
            "Requirement already satisfied: python-dateutil>=2.7 in /usr/local/lib/python3.10/dist-packages (from matplotlib) (2.8.2)\n",
            "Requirement already satisfied: six>=1.5 in /usr/local/lib/python3.10/dist-packages (from python-dateutil>=2.7->matplotlib) (1.16.0)\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "s1J3sYlG9uUr"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "Gfj_VeNH9uZE"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "import PyPDF2  # Import PyPDF2 for reading PDF files"
      ],
      "metadata": {
        "id": "wB2o4xqm9udu"
      },
      "execution_count": 4,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "import os  # Import os for file and directory operations"
      ],
      "metadata": {
        "id": "espvxhnW9ugF"
      },
      "execution_count": 5,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "import re  # Import re for regular expressions"
      ],
      "metadata": {
        "id": "portaafO9ukO"
      },
      "execution_count": 6,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "import nltk  # Import nltk for natural language processing"
      ],
      "metadata": {
        "id": "PJlQFfxU9889"
      },
      "execution_count": 7,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "from nltk.corpus import stopwords  # Import stopwords for removing common words from text"
      ],
      "metadata": {
        "id": "Ww0A_Bla98_7"
      },
      "execution_count": 8,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "from nltk.tokenize import word_tokenize  # Import word_tokenize for splitting text into words"
      ],
      "metadata": {
        "id": "u9qzo0n399Cj"
      },
      "execution_count": 9,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "from collections import Counter  # Import Counter for counting word frequencies"
      ],
      "metadata": {
        "id": "J1LNdQaf99E_"
      },
      "execution_count": 10,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "import matplotlib.pyplot as plt  # Import matplotlib for plotting graphs"
      ],
      "metadata": {
        "id": "dJu4EKpO99Hr"
      },
      "execution_count": 11,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "from textblob import TextBlob  # Import TextBlob for sentiment analysis"
      ],
      "metadata": {
        "id": "FCj9_Dk499Kf"
      },
      "execution_count": 12,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "ip5ZCXug-K1l"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "4LytTRcw-K41"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# Ensure required NLTK data is downloaded"
      ],
      "metadata": {
        "id": "qhyYbQ8A-K71"
      },
      "execution_count": 13,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "nltk.download('punkt')  # Download the punkt tokenizer models"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "PyncJAlS-K-9",
        "outputId": "c49d7905-71fa-43b2-a29d-54ac3e030c2e"
      },
      "execution_count": 15,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "[nltk_data] Downloading package punkt to /root/nltk_data...\n",
            "[nltk_data]   Package punkt is already up-to-date!\n"
          ]
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "True"
            ]
          },
          "metadata": {},
          "execution_count": 15
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "nltk.download('stopwords')  # Download the stopwords corpus"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "ivGa5k3Y-LBn",
        "outputId": "aa032549-29dc-4094-8b13-90c6c373ccf7"
      },
      "execution_count": 16,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "[nltk_data] Downloading package stopwords to /root/nltk_data...\n",
            "[nltk_data]   Package stopwords is already up-to-date!\n"
          ]
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "True"
            ]
          },
          "metadata": {},
          "execution_count": 16
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "8Q0ao2HG-LEn"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "P9Q-36Cv-LKZ"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# Path to the PDF file"
      ],
      "metadata": {
        "id": "kuGVPXtN-LNV"
      },
      "execution_count": 17,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "pdf_path = \"/content/ERIC MWANIKI  DS & ML RESUME.pdf\"  # Define the path to the PDF file"
      ],
      "metadata": {
        "id": "_OgudBGA-Vn8"
      },
      "execution_count": 18,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "mbC403WD-VrT"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "zgIEVoNC-VuL"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# Directory to save results"
      ],
      "metadata": {
        "id": "ueKzkL_I-VzG"
      },
      "execution_count": 19,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "output_dir = \"/content/resume_analysis/\"  # Define the directory to save results"
      ],
      "metadata": {
        "id": "MlXfH1jb-V31"
      },
      "execution_count": 20,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "os.makedirs(output_dir, exist_ok=True)  # Create the directory if it doesn't exist"
      ],
      "metadata": {
        "id": "jMuwKx96-V79"
      },
      "execution_count": 21,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "YokLGvQG-fHd"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "tqgadoDi-fKu"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# Read PDF file and extract text"
      ],
      "metadata": {
        "id": "V4eR_SzK-fNc"
      },
      "execution_count": 22,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "text = \"\"  # Initialize an empty string to hold the text"
      ],
      "metadata": {
        "id": "LQkxPg9e-fP-"
      },
      "execution_count": 23,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "with open(pdf_path, \"rb\") as file:  # Open the PDF file in binary read mode\n",
        "    reader = PyPDF2.PdfReader(file)  # Create a PdfReader object\n",
        "    for page in reader.pages:  # Iterate through each page in the PDF\n",
        "        text += page.extract_text()  # Extract text from the page and append to the text variable"
      ],
      "metadata": {
        "id": "Y_677aZ3-fSk"
      },
      "execution_count": 24,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "87PqrJjt-m7k"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# Display extracted text"
      ],
      "metadata": {
        "id": "ec8OKNkg-m-8"
      },
      "execution_count": 25,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "print(\"Extracted Text:\")  # Print header for extracted text\n",
        "print(text[:2000])  # Displaying first 2000 characters for brevity"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "I7z4CM6j-nCH",
        "outputId": "7f529ee3-655f-41ab-b1cd-2cc982acb937"
      },
      "execution_count": 26,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Extracted Text:\n",
            "ERIC MWANIKI RESUME  \n",
            " \n",
            "Eric Mwaniki  \n",
            "P.O. BOX 90420 -80100, Mombasa, Kenya  \n",
            "Email: ericmwaniki2004@gmail.com  \n",
            "GitHub: https://github.com/Eric428 -a \n",
            " \n",
            "Objective : \n",
            "To leverage my strong foundation in Civil Engineering with advanced skills in data science and \n",
            "machine learning to drive innovation and efficiency in infrastructure development and \n",
            "management.  \n",
            " \n",
            "Education:  \n",
            "Online Data Science and Machine Learning Studies  \n",
            "Kaggle and Udemy, Completed in May 2023  \n",
            " Gained comprehen sive knowledge in data science, machine learning, and statistical \n",
            "analysis.  \n",
            " Executed hands -on projects demonstrating practical application of data science skills.  \n",
            " \n",
            "Bachelor of Science in Civil Engineering  \n",
            "Technical University of Mombasa, Mombasa, Kenya  \n",
            "Expected Graduation: 2028  \n",
            " \n",
            "Relevant Coursework : \n",
            "Computer programming  \n",
            "CAD Drafting and Programming  \n",
            "Structural Analysis  \n",
            "Geotechnical Engineering  \n",
            "Fluid Mechanics  Construction Management  \n",
            " \n",
            "Integration of Data Science and Machine Learning:  \n",
            "I am passionate about integrating data science and machine learning principles into civil \n",
            "engineering practices to enhance project planning, design, and implementation. By leveraging \n",
            "data analytics, I aim to optimize construction processes, improve structural performance \n",
            "predic tions, and implement predictive maintenance strategies. My proficiency in Python \n",
            "(NumPy, Pandas, Scikit -learn) and R allows me to utilize advanced statistical techniques for \n",
            "data -driven decision -making in infrastructure projects.  \n",
            " \n",
            "Experience:  \n",
            " \n",
            "Chief Conten t Officer  \n",
            "Ignitus Organization  \n",
            "July 2024 - Present  \n",
            " \n",
            " Oversee the development and implementation of content strategies that integrate \n",
            "machine learning models to enhance content personalization and engagement.  \n",
            " Lead a team of content creators, data analysts, and machine learning engineers to \n",
            "develop data -driven content solutions.  \n",
            " Design and deploy machine learning algorithms for content recommendation, user \n",
            "engagement \n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "G7WjtD1a-tTr"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "U8XL8FOL-tXS"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "eByVQWtx-taa"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# Clean and process text"
      ],
      "metadata": {
        "id": "mD7lwfjx-tdC"
      },
      "execution_count": 27,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "text = re.sub(r'\\s+', ' ', text).strip()  # Replace multiple whitespace characters with a single space and strip leading/trailing spaces\n"
      ],
      "metadata": {
        "id": "nPJY2MXV-tfk"
      },
      "execution_count": 28,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "words = word_tokenize(text.lower())  # Tokenize the text into words and convert to lowercase"
      ],
      "metadata": {
        "id": "slGIa-sd-tiz"
      },
      "execution_count": 29,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "words = [word for word in words if word.isalnum() and word not in stopwords.words('english')]  # Remove non-alphanumeric words and stopwords\n"
      ],
      "metadata": {
        "id": "zHS9nxjx-nFD"
      },
      "execution_count": 30,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "m8g_RfyN-fU9"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# Display cleaned words"
      ],
      "metadata": {
        "id": "vDnkxzcv-fXS"
      },
      "execution_count": 31,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "print(\"\\nCleaned Words Sample:\")  # Print header for cleaned words\n",
        "print(words[:50])  # Displaying first 50 cleaned words"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "CjIFQQmH-fZt",
        "outputId": "d7aa0d4d-5cb6-4034-ea5c-47130ae1afe3"
      },
      "execution_count": 32,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "\n",
            "Cleaned Words Sample:\n",
            "['eric', 'mwaniki', 'resume', 'eric', 'mwaniki', 'box', '90420', 'mombasa', 'kenya', 'email', 'ericmwaniki2004', 'github', 'https', 'objective', 'leverage', 'strong', 'foundation', 'civil', 'engineering', 'advanced', 'skills', 'data', 'science', 'machine', 'learning', 'drive', 'innovation', 'efficiency', 'infrastructure', 'development', 'management', 'education', 'online', 'data', 'science', 'machine', 'learning', 'studies', 'kaggle', 'udemy', 'completed', 'may', '2023', 'gained', 'comprehen', 'sive', 'knowledge', 'data', 'science', 'machine']\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "8w2epTHm-V_F"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "H8lWIgjZ-WCY"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "CS0gy0ov-WFe"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# Count word frequency"
      ],
      "metadata": {
        "id": "LIUVnlR299NN"
      },
      "execution_count": 33,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "word_freq = Counter(words)  # Count the frequency of each word"
      ],
      "metadata": {
        "id": "EpxIfApB9uq-"
      },
      "execution_count": 34,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "most_common_words = word_freq.most_common(20)  # Get the 20 most common words"
      ],
      "metadata": {
        "id": "8UlQUqJk9utr"
      },
      "execution_count": 35,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "MeW6xS109uwT"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "OiHR12Ti_LJs"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# Display most common words"
      ],
      "metadata": {
        "id": "FMhErLJ49uyb"
      },
      "execution_count": 36,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "print(\"\\nMost Common Words:\")  # Print header for most common words\n",
        "for word, freq in most_common_words:  # Iterate through the most common words\n",
        "    print(f\"{word}: {freq}\")  # Print each word and its frequency"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "jSepGz9J9u0r",
        "outputId": "f85bded7-a39b-44be-a027-60a0423d4a74"
      },
      "execution_count": 39,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "\n",
            "Most Common Words:\n",
            "data: 32\n",
            "learning: 21\n",
            "machine: 18\n",
            "science: 13\n",
            "analysis: 9\n",
            "content: 8\n",
            "projects: 7\n",
            "project: 7\n",
            "engineering: 6\n",
            "programming: 6\n",
            "predictive: 6\n",
            "statistical: 5\n",
            "techniques: 5\n",
            "present: 5\n",
            "models: 5\n",
            "model: 5\n",
            "advanced: 4\n",
            "skills: 4\n",
            "development: 4\n",
            "management: 4\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "0tuzgsxa9u3e"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "it_cT-sX9u5K"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "IzbYk7Hu9u7z"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# Plot most common words"
      ],
      "metadata": {
        "id": "7aCje5ea9vCU"
      },
      "execution_count": 40,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "plt.figure(figsize=(12, 8))  # Create a figure with a specific size\n",
        "plt.bar([word[0] for word in most_common_words], [word[1] for word in most_common_words])  # Create a bar chart\n",
        "plt.title('Most Common Words in Resume')  # Set the title of the plot\n",
        "plt.xlabel('Words')  # Set the x-axis label\n",
        "plt.ylabel('Frequency')  # Set the y-axis label\n",
        "plt.xticks(rotation=45)  # Rotate x-axis labels for better readability\n",
        "plt.tight_layout()  # Adjust layout to prevent clipping\n",
        "plt.show()  # Display the plot"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 642
        },
        "id": "nQzXLTh19vGr",
        "outputId": "41a5dd81-012b-4741-95f3-da4beb6a3c63"
      },
      "execution_count": 41,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 1200x800 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "l1_m0DJt9vJN"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "rhEVSO_A9vQ9"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# Extract sections from text with error handling"
      ],
      "metadata": {
        "id": "0LvXPbyx9vTl"
      },
      "execution_count": 42,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "def extract_section(title):  # Define a function to extract sections\n",
        "    pattern = re.compile(rf'{title}:(.*?)(Objective:|Education:|Experience:|Skills:|Projects:|Achievements:|References:|$)', re.S)  # Compile a regex pattern to extract the section\n",
        "    match = pattern.search(text)  # Search for the pattern in the text\n",
        "    return match.group(1).strip() if match else \"Not Found\"  # Return the section content if found, otherwise return \"Not Found\"\n",
        ""
      ],
      "metadata": {
        "id": "DsiZbHDx9vWT"
      },
      "execution_count": 43,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "3Lz9z1pY9vYU"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "sections = {  # Dictionary to hold extracted sections\n",
        "    \"Objective\": extract_section(\"Objective\"),  # Extract the Objective section\n",
        "    \"Education\": extract_section(\"Education\"),  # Extract the Education section\n",
        "    \"Experience\": extract_section(\"Experience\"),  # Extract the Experience section\n",
        "    \"Skills\": extract_section(\"Skills\"),  # Extract the Skills section\n",
        "    \"Projects\": extract_section(\"Projects\"),  # Extract the Projects section\n",
        "    \"Achievements\": extract_section(\"Achievements\"),  # Extract the Achievements section\n",
        "    \"References\": extract_section(\"References\"),  # Extract the References section\n",
        "}"
      ],
      "metadata": {
        "id": "-W6I-tkJ9val"
      },
      "execution_count": 44,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "QcWSdIP19vdB"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# Display and save sections"
      ],
      "metadata": {
        "id": "E9MS4h-w9ve9"
      },
      "execution_count": 45,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "for section, content in sections.items():  # Iterate through each section and its content\n",
        "    print(f\"\\n{section} Section:\")  # Print header for the section\n",
        "    print(content[:500])  # Displaying first 500 characters for brevity\n",
        "\n",
        "    # Save each section to a text file\n",
        "    with open(os.path.join(output_dir, f\"{section.lower()}.txt\"), \"w\") as file:  # Open a file to write the section content\n",
        "        file.write(content)  # Write the section content to the file"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "u5FCQKga9vhW",
        "outputId": "39de869e-a919-4799-b6da-3d030c11ffb2"
      },
      "execution_count": 46,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "\n",
            "Objective Section:\n",
            "Not Found\n",
            "\n",
            "Education Section:\n",
            "Online Data Science and Machine Learning Studies Kaggle and Udemy, Completed in May 2023  Gained comprehen sive knowledge in data science, machine learning, and statistical analysis.  Executed hands -on projects demonstrating practical application of data science skills. Bachelor of Science in Civil Engineering Technical University of Mombasa, Mombasa, Kenya Expected Graduation: 2028 Relevant Coursework : Computer programming CAD Drafting and Programming Structural Analysis Geotechnical Engine\n",
            "\n",
            "Experience Section:\n",
            "Chief Conten t Officer Ignitus Organization July 2024 - Present  Oversee the development and implementation of content strategies that integrate machine learning models to enhance content personalization and engagement.  Lead a team of content creators, data analysts, and machine learning engineers to develop data -driven content solutions.  Design and deploy machine learning algorithms for content recommendation, user engagement analysis, and performance optimization.  Utilize natural langu\n",
            "\n",
            "Skills Section:\n",
            " Data Science & Machine Learning: Regression, Classification, Clustering, Neural Networks  Programming Languages: Python (NumPy, Pandas, Scikit -learn), R  Data Manipulation & Visualization: SQL, Matplotlib, Seaborn  Deep Learning Frameworks: TensorFlow, Keras, PyTorch  Data Processing: Spark, Hadoop  Statistical Analysis: Hypothesis Testing, A/B Testing  Tools: Jupyter, Git, Docker, Colab Professional Development: Actively engage in continuous learni ng and exploration of emerging trends\n",
            "\n",
            "Projects Section:\n",
            " Predictive Maintenance Model: Developed a machine learning model predicting equipment failures, reducing downtime by 30%.  Sentiment Analysis on Social Media Data: Conducted sentiment analysis on Twitt er data using natural language processing techniques.\n",
            "\n",
            "Achievements Section:\n",
            " Successfully implemented a predictive maintenance model that reduced equipment downtime by 25%, ensuring smoother project ex ecution and cost management.  Contributed to the development of a real -time data dashboard for project monitoring, enabling proactive decision -making and risk mitigation strategies.  Received commendation for innovative problem -solving and proactive contribu tion to the team's data science initiatives.\n",
            "\n",
            "References Section:\n",
            "Afelio Padilla COO, Ignitus Email: socialignitus@gmail.com Alternative Email: afelio@cpturz.net\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "BDz4Tj8N9vjt"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "XS_oX32Z9vlc"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# Perform sentiment analysis"
      ],
      "metadata": {
        "id": "IU31lLbc9vnr"
      },
      "execution_count": 47,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "sentiment_scores = {}  # Dictionary to hold sentiment scores for each section"
      ],
      "metadata": {
        "id": "EmOCtFDR9vqU"
      },
      "execution_count": 48,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "for section, content in sections.items():  # Iterate through each section and its content\n",
        "    blob = TextBlob(content)  # Create a TextBlob object for sentiment analysis\n",
        "    sentiment_scores[section] = {  # Store sentiment scores for the section\n",
        "        \"Polarity\": blob.sentiment.polarity,  # Get polarity score\n",
        "        \"Subjectivity\": blob.sentiment.subjectivity  # Get subjectivity score\n",
        "    }"
      ],
      "metadata": {
        "id": "avAfGfX-9vsF"
      },
      "execution_count": 49,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "xQmHtVbD9vud"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# Display sentiment scores"
      ],
      "metadata": {
        "id": "LcBXHr4X9vwk"
      },
      "execution_count": 50,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "print(\"\\nSentiment Analysis Scores:\")  # Print header for sentiment analysis scores\n",
        "for section, scores in sentiment_scores.items():  # Iterate through each section and its sentiment scores\n",
        "    print(f\"{section}: Polarity={scores['Polarity']}, Subjectivity={scores['Subjectivity']}\")  # Print sentiment scores for the section\n",
        ""
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "Z4TAVUPN9v07",
        "outputId": "a3572f46-7abf-42ae-f42c-f375c800393d"
      },
      "execution_count": 51,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "\n",
            "Sentiment Analysis Scores:\n",
            "Objective: Polarity=0.0, Subjectivity=0.0\n",
            "Education: Polarity=0.10833333333333335, Subjectivity=0.4916666666666667\n",
            "Experience: Polarity=0.10636363636363637, Subjectivity=0.248013468013468\n",
            "Skills: Polarity=-0.011111111111111108, Subjectivity=0.3666666666666667\n",
            "Projects: Polarity=0.07777777777777778, Subjectivity=0.25555555555555554\n",
            "Achievements: Polarity=0.48333333333333334, Subjectivity=0.75\n",
            "References: Polarity=0.0, Subjectivity=0.0\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "e9kfsX1o9v3O"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "5TWu0_5D9v5E"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# Save sentiment analysis results"
      ],
      "metadata": {
        "id": "IJTAKr1d9v7b"
      },
      "execution_count": 52,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "with open(os.path.join(output_dir, \"sentiment_analysis.txt\"), \"w\") as file:  # Open a file to write sentiment analysis results\n",
        "    for section, scores in sentiment_scores.items():  # Iterate through each section and its sentiment scores\n",
        "        file.write(f\"{section}: Polarity={scores['Polarity']}, Subjectivity={scores['Subjectivity']}\\n\")  # Write sentiment scores to the file\n",
        ""
      ],
      "metadata": {
        "id": "u2op4gBE9wAb"
      },
      "execution_count": 53,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "t21mkI-BADMs"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "XEjYc76a9wCz"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# Plot section distribution"
      ],
      "metadata": {
        "id": "6qeg5k1K9wE2"
      },
      "execution_count": 54,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "section_counts = {section: len(content.split()) for section, content in sections.items()}  # Count the number of words in each section\n",
        "plt.figure(figsize=(10, 7))  # Create a figure with a specific size\n",
        "plt.pie(section_counts.values(), labels=section_counts.keys(), autopct='%1.1f%%', startangle=140)  # Create a pie chart of section distribution\n",
        "plt.title('Distribution of Resume Sections')  # Set the title of the plot\n",
        "plt.show()  # Display the plot"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 598
        },
        "id": "hYHga5j99wJN",
        "outputId": "a9c08c4f-1e6a-4d18-8258-e87a6bc59750"
      },
      "execution_count": 55,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 1000x700 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "23MKiOEe9wLz"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "jUbCEHOy9wN2"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# Save section distribution plot"
      ],
      "metadata": {
        "id": "dF4gjzwb9waV"
      },
      "execution_count": 56,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "plt.figure(figsize=(10, 7))  # Create a figure with a specific size\n",
        "plt.pie(section_counts.values(), labels=section_counts.keys(), autopct='%1.1f%%', startangle=140)  # Create a pie chart of section distribution\n",
        "plt.title('Distribution of Resume Sections')  # Set the title of the plot\n",
        "plt.savefig(os.path.join(output_dir, 'section_distribution.png'))  # Save the pie chart as an image file"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 598
        },
        "id": "47Drp1Nv9wdD",
        "outputId": "c1556a0b-02b7-4bbf-9833-ee3829f71bd1"
      },
      "execution_count": 57,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 1000x700 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "ed1YMzhW9wfV"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "1Hx1LMJ89wiT"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "BsM-cB8PE38V"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "# CONTINUED ANALYSIS"
      ],
      "metadata": {
        "id": "icO_d1PzE4k0"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# Define key skills and requirements for a Data Science position"
      ],
      "metadata": {
        "id": "6YXYIpEoE88X"
      },
      "execution_count": 63,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "key_skills_data_science = [\n",
        "    \"data science\", \"python\", \"machine learning\", \"statistical analysis\", \"data analysis\",\n",
        "    \"deep learning\", \"predictive modeling\", \"sql\", \"data visualization\", \"pandas\", \"numpy\",\n",
        "    \"scikit-learn\", \"r\", \"data mining\", \"big data\", \"data wrangling\"\n",
        "]  # List of key skills for Data Science positions"
      ],
      "metadata": {
        "id": "xOEVFQkf9wk0"
      },
      "execution_count": 64,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "5K9qNyIJE1_B"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "qmsOIeLxEiVE"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# Resume text extracted from a sample resume"
      ],
      "metadata": {
        "id": "Rdxa5aszD4a0"
      },
      "execution_count": 65,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "resume_text = \"\"\"\n",
        "    JOHN DOE RESUME\n",
        "\n",
        "    John Doe\n",
        "    123 Data Science Lane, Data City, CA, USA\n",
        "    Email: john.doe@example.com\n",
        "    GitHub: https://github.com/johndoe\n",
        "\n",
        "    Objective:\n",
        "    To leverage my expertise in data science, machine learning, and statistical analysis to drive data-driven decision-making and optimize business processes in a challenging Data Scientist role.\n",
        "\n",
        "    Education:\n",
        "    Master of Science in Data Science\n",
        "    University of Data, Data City, CA, USA\n",
        "    Graduated: 2023\n",
        "\n",
        "    Bachelor of Science in Computer Science\n",
        "    Tech University, Data City, CA, USA\n",
        "    Graduated: 2021\n",
        "\n",
        "    Key Skills:\n",
        "    - Data Science\n",
        "    - Python (NumPy, Pandas, Scikit-learn)\n",
        "    - Machine Learning (Classification, Regression, Clustering)\n",
        "    - Statistical Analysis\n",
        "    - Deep Learning (TensorFlow, Keras)\n",
        "    - SQL\n",
        "    - Data Visualization (Matplotlib, Seaborn)\n",
        "    - Big Data (Hadoop, Spark)\n",
        "    - Data Wrangling\n",
        "    - Predictive Modeling\n",
        "\n",
        "    Experience:\n",
        "    Data Scientist\n",
        "    Innovative Data Solutions\n",
        "    June 2023 - Present\n",
        "    - Developed and deployed machine learning models to enhance predictive analytics for client projects.\n",
        "    - Utilized Python and SQL to analyze and visualize large datasets, providing actionable insights.\n",
        "    - Collaborated with cross-functional teams to implement data-driven strategies and solutions.\n",
        "    - Led initiatives in data wrangling, feature engineering, and model tuning to optimize performance.\n",
        "\n",
        "    Machine Learning Intern\n",
        "    Tech Innovations Inc.\n",
        "    June 2022 - May 2023\n",
        "    - Assisted in building and evaluating machine learning models for various business applications.\n",
        "    - Performed data preprocessing and feature selection to improve model accuracy.\n",
        "    - Created data visualizations to present findings and support decision-making processes.\n",
        "\n",
        "    Projects:\n",
        "    - Customer Churn Prediction: Developed a machine learning model to predict customer churn, resulting in a 15% improvement in retention strategies.\n",
        "    - Sales Forecasting: Implemented a time series forecasting model to predict sales trends, achieving a 10% increase in forecast accuracy.\n",
        "\n",
        "    Certifications:\n",
        "    - Certified Data Scientist (Data Science Council)\n",
        "    - Machine Learning Specialization (Coursera)\n",
        "\"\"\"  # Resume text extracted from the example resume"
      ],
      "metadata": {
        "id": "Uj67dlmdDsim"
      },
      "execution_count": 66,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "XZo6k73MDdFu"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "e55325ZDF-a9"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# Function to calculate skill match percentage for Data Science"
      ],
      "metadata": {
        "id": "xdvyydNX9wsL"
      },
      "execution_count": 67,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# Convert resume text to lowercase for case-insensitive matching"
      ],
      "metadata": {
        "id": "BNKq-k1o9wyr"
      },
      "execution_count": 69,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "text_lower = resume_text.lower()  # Convert all text to lowercase to ensure case-insensitive search"
      ],
      "metadata": {
        "id": "8ifpdtO3F6wl"
      },
      "execution_count": 70,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "qi8S7fWvF61w"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# Total number of key skills"
      ],
      "metadata": {
        "id": "mw7mZvXbF65e"
      },
      "execution_count": 71,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "total_skills = len(key_skills_data_science)  # Count the total number of key skills defined"
      ],
      "metadata": {
        "id": "vWJ8XMIUF68n"
      },
      "execution_count": 72,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "CWmZ9jclGFuU"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# Count the number of matched skills"
      ],
      "metadata": {
        "id": "_G_bZBarGFxV"
      },
      "execution_count": 73,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "matched_skills = sum(1 for skill in key_skills_data_science if re.search(r'\\b' + re.escape(skill) + r'\\b', text_lower))  # Count skills found in the resume text\n"
      ],
      "metadata": {
        "id": "xhRdE_DJGF0Y"
      },
      "execution_count": 74,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "ZAepP5LFGF2w"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# Calculate percentage of matched skills"
      ],
      "metadata": {
        "id": "3etUhFagGF5e"
      },
      "execution_count": 75,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "suitability_percentage_data_science = (matched_skills / total_skills) * 100  # Calculate the percentage of skills that match\n"
      ],
      "metadata": {
        "id": "dkDwrX1DGNLe"
      },
      "execution_count": 76,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "IPlOQTgWGNO5"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# Print the suitability percentage formatted to two decimal places"
      ],
      "metadata": {
        "id": "lgwawgdeGNRR"
      },
      "execution_count": 77,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "print(f\"Suitability Percentage for Data Science Position: {suitability_percentage_data_science:.2f}%\")  # Output the percentage with two decimal places\n"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "WdHO82AQGNTt",
        "outputId": "ad0a0abf-e912-4e64-f159-f8a8cfb983f4"
      },
      "execution_count": 78,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Suitability Percentage for Data Science Position: 81.25%\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "HD3p7bRVGWY9"
      },
      "execution_count": null,
      "outputs": []
    }
  ]
}