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    "name": "Naina Naina",
    "label": "Entry-level IT position at Amazon",
    "summary": "Entry-level IT professional with expertise in Python, Java, and web technologies including HTML5 and CSS. Proven ability in data analysis and project management, successfully leading projects that enhance operational efficiency. Focused on leveraging technical skills and critical thinking to drive innovative solutions at Amazon. Recognized for exceptional communication skills, fostering collaboration across diverse teams.",
    "location": "INDIA",
    "url": "https://naina-k.github.io/",
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        "network": "LinkedIn",
        "username": "naina-912905269",
        "url": "https://linkedin.com/in/naina-912905269"
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      {
        "network": "GitHub",
        "username": "naina-k",
        "url": "https://github.com/naina-k"
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  "education": [
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      "institution": "Chaudhary Charan Singh University",
      "degree": "BCA",
      "field": "COMPUTER APPLICATION (IT)",
      "startDate": "2022",
      "endDate": "2025",
      "score": "7.18"
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      "institution": "Indira Gandhi Delhi Technical University for Women (IGDTUW)",
      "degree": "MCA",
      "field": "IT",
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      "name": "FAKE NEWS DETECTOR USING MACHINE LEARNING",
      "description": "Fake News Detector | Machine Learning Project\n\nDeveloped a machine learning-based application to classify news articles as Real or Fake.\nPreprocessed and analyzed text data using techniques such as tokenization, stop-word removal, and TF-IDF vectorization.\nTrained and evaluated a classification model to identify potentially misleading or false news.\nBuilt a simple user interface where users can enter news content and receive a prediction.",
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      "name": "Accident Prediction System (AIML)",
      "description": "Accident Prediction System | Python, Machine Learning\n\nDeveloped an AI/ML-based system to predict the likelihood of road accidents using historical accident and environmental data.\nPerformed data preprocessing, feature selection, and exploratory data analysis to identify factors associated with accident occurrence.\nTrained and evaluated machine learning classification models to predict accident risk based on input conditions.\nBuilt a user-friendly prediction interface that provides an accident-risk prediction based on the entered parameters.",
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        "Matplotlib/Seaborn"
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      "name": "Secure File Upload & Malware Scanning Portal",
      "description": "Secure File Upload & Malware Scanning Portal | Python, Flask, Cybersecurity\n\nDeveloped a secure web-based portal for file uploading and malware detection to reduce the risk of malicious files entering a system.\nImplemented file validation, extension/type checking, size restrictions, and secure upload handling to prevent unsafe file uploads.\nIntegrated malware scanning to analyze uploaded files and identify potentially malicious or suspicious content.\nDesigned a user-friendly interface to display upload status, scan results, and security alerts.\nApplied basic cybersecurity practices such as input validation, access control, and secure file storage.",
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      "date": "2026-03"
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      "date": "2026-06"
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