{"metadata":{"kernelspec":{"language":"python","display_name":"Python 3","name":"python3"},"language_info":{"name":"python","version":"3.11.11","mimetype":"text/x-python","codemirror_mode":{"name":"ipython","version":3},"pygments_lexer":"ipython3","nbconvert_exporter":"python","file_extension":".py"},"kaggle":{"accelerator":"none","dataSources":[{"sourceId":3004,"databundleVersionId":861823,"sourceType":"competition"}],"dockerImageVersionId":31012,"isInternetEnabled":true,"language":"python","sourceType":"notebook","isGpuEnabled":false}},"nbformat_minor":4,"nbformat":4,"cells":[{"cell_type":"code","source":"import numpy as np\nimport pandas as pd\nimport torch\nimport torch.nn as nn\nimport torch.optim as optim\nfrom torch.utils.data import Dataset, DataLoader\nfrom sklearn.model_selection import train_test_split\nfrom torchvision import transforms\n","metadata":{"_uuid":"8f2839f25d086af736a60e9eeb907d3b93b6e0e5","_cell_guid":"b1076dfc-b9ad-4769-8c92-a6c4dae69d19","trusted":true,"execution":{"iopub.status.busy":"2025-05-01T10:02:37.248357Z","iopub.execute_input":"2025-05-01T10:02:37.248672Z","iopub.status.idle":"2025-05-01T10:02:49.509667Z","shell.execute_reply.started":"2025-05-01T10:02:37.248638Z","shell.execute_reply":"2025-05-01T10:02:49.508804Z"}},"outputs":[],"execution_count":null},{"cell_type":"code","source":"class DigitDataset(Dataset):\n    def __init__(self, images, labels=None, transform=None):\n        self.images = images\n        self.labels = labels\n        self.transform = transform\n\n    def __len__(self):\n        return len(self.images)\n\n    def __getitem__(self, idx):\n        image = self.images[idx].reshape(28, 28).astype(np.uint8)\n        if self.transform:\n            image = self.transform(image)\n        if self.labels is not None:\n            return image, self.labels[idx]\n        return image\n","metadata":{"trusted":true,"execution":{"iopub.status.busy":"2025-05-01T10:03:19.790994Z","iopub.execute_input":"2025-05-01T10:03:19.791445Z","iopub.status.idle":"2025-05-01T10:03:19.800700Z","shell.execute_reply.started":"2025-05-01T10:03:19.791408Z","shell.execute_reply":"2025-05-01T10:03:19.799705Z"}},"outputs":[],"execution_count":null},{"cell_type":"code","source":"# Load CSVs\ndf = pd.read_csv('/kaggle/input/digit-recognizer/train.csv')\ntest_df = pd.read_csv('/kaggle/input/digit-recognizer/test.csv')\n\n# Split\nX = df.drop('label', axis=1).values\ny = df['label'].values\nX_train, X_val, y_train, y_val = train_test_split(X, y, test_size=0.1, random_state=42)\n\n# Transforms\ntransform = transforms.Compose([\n    transforms.ToPILImage(),\n    transforms.ToTensor(),\n])\n\n# Datasets and Loaders\ntrain_ds = DigitDataset(X_train, y_train, transform)\nval_ds = DigitDataset(X_val, y_val, transform)\ntest_ds = DigitDataset(test_df.values, transform=transform)\n\ntrain_loader = DataLoader(train_ds, batch_size=64, shuffle=True)\nval_loader = DataLoader(val_ds, batch_size=64, shuffle=False)\ntest_loader = DataLoader(test_ds, batch_size=64, shuffle=False)\n","metadata":{"trusted":true,"execution":{"iopub.status.busy":"2025-05-01T10:03:22.505616Z","iopub.execute_input":"2025-05-01T10:03:22.505936Z","iopub.status.idle":"2025-05-01T10:03:28.690023Z","shell.execute_reply.started":"2025-05-01T10:03:22.505912Z","shell.execute_reply":"2025-05-01T10:03:28.689108Z"}},"outputs":[],"execution_count":null},{"cell_type":"code","source":"# Load CSVs\ndf = pd.read_csv('/kaggle/input/digit-recognizer/train.csv')\ntest_df = pd.read_csv('/kaggle/input/digit-recognizer/test.csv')\n\n# Split\nX = df.drop('label', axis=1).values\ny = df['label'].values\nX_train, X_val, y_train, y_val = train_test_split(X, y, test_size=0.1, random_state=42)\n\n# Transforms\ntransform = transforms.Compose([\n    transforms.ToPILImage(),\n    transforms.ToTensor(),\n])\n\n# Datasets and Loaders\ntrain_ds = DigitDataset(X_train, y_train, transform)\nval_ds = DigitDataset(X_val, y_val, transform)\ntest_ds = DigitDataset(test_df.values, transform=transform)\n\ntrain_loader = DataLoader(train_ds, batch_size=64, shuffle=True)\nval_loader = DataLoader(val_ds, batch_size=64, shuffle=False)\ntest_loader = DataLoader(test_ds, batch_size=64, shuffle=False)\n","metadata":{"trusted":true,"execution":{"iopub.status.busy":"2025-05-01T10:03:31.579065Z","iopub.execute_input":"2025-05-01T10:03:31.579470Z","iopub.status.idle":"2025-05-01T10:03:36.067979Z","shell.execute_reply.started":"2025-05-01T10:03:31.579438Z","shell.execute_reply":"2025-05-01T10:03:36.066905Z"}},"outputs":[],"execution_count":null},{"cell_type":"code","source":"class CNN(nn.Module):\n    def __init__(self):\n        super(CNN, self).__init__()\n        self.conv_layers = nn.Sequential(\n            nn.Conv2d(1, 64, kernel_size=3, padding=1),\n            nn.BatchNorm2d(64),\n            nn.ReLU(),\n            nn.MaxPool2d(2),\n\n            nn.Conv2d(64, 128, kernel_size=3, padding=1),\n            nn.BatchNorm2d(128),\n            nn.ReLU(),\n            nn.MaxPool2d(2),\n\n            nn.Conv2d(128, 256, kernel_size=3, padding=1),\n            nn.BatchNorm2d(256),\n            nn.ReLU(),\n            nn.AdaptiveAvgPool2d((3, 3)),\n        )\n        self.fc_layers = nn.Sequential(\n            nn.Flatten(),\n            nn.Linear(256 * 3 * 3, 256),\n            nn.ReLU(),\n            nn.Dropout(0.4),\n            nn.Linear(256, 10)\n        )\n\n    def forward(self, x):\n        x = self.conv_layers(x)\n        x = self.fc_layers(x)\n        return x\n","metadata":{"trusted":true,"execution":{"iopub.status.busy":"2025-05-01T10:56:02.232973Z","iopub.execute_input":"2025-05-01T10:56:02.233292Z","iopub.status.idle":"2025-05-01T10:56:02.240355Z","shell.execute_reply.started":"2025-05-01T10:56:02.233269Z","shell.execute_reply":"2025-05-01T10:56:02.239440Z"}},"outputs":[],"execution_count":null},{"cell_type":"code","source":"device = torch.device(\"cuda\" if torch.cuda.is_available() else \"cpu\")\nmodel = CNN().to(device)\ncriterion = nn.CrossEntropyLoss()\noptimizer = optim.Adam(model.parameters(), lr=0.001)\n\nepochs = 10\nfor epoch in range(epochs):\n    model.train()\n    total_loss = 0\n    for images, labels in train_loader:\n        images, labels = images.to(device), labels.to(device)\n        images = images.view(-1, 1, 28, 28)\n\n        optimizer.zero_grad()\n        outputs = model(images)\n        loss = criterion(outputs, labels)\n        loss.backward()\n        optimizer.step()\n        total_loss += loss.item()\n\n    model.eval()\n    correct = 0\n    total = 0\n    with torch.no_grad():\n        for images, labels in val_loader:\n            images, labels = images.to(device), labels.to(device)\n            images = images.view(-1, 1, 28, 28)\n            outputs = model(images)\n            _, preds = torch.max(outputs, 1)\n            correct += (preds == labels).sum().item()\n            total += labels.size(0)\n\n    print(f\"Epoch {epoch+1}/{epochs}, Loss: {total_loss:.4f}, Val Acc: {correct/total:.4f}\")\n","metadata":{"trusted":true,"execution":{"iopub.status.busy":"2025-05-01T10:56:04.153660Z","iopub.execute_input":"2025-05-01T10:56:04.154059Z"}},"outputs":[],"execution_count":null},{"cell_type":"code","source":"model.eval()\npredictions = []\nwith torch.no_grad():\n    for images in test_loader:\n        images = images.to(device)\n        images = images.view(-1, 1, 28, 28)\n        outputs = model(images)\n        _, preds = torch.max(outputs, 1)\n        predictions.extend(preds.cpu().numpy())\n\nsubmission = pd.DataFrame({\n    \"ImageId\": np.arange(1, len(predictions)+1),\n    \"Label\": predictions\n})\nsubmission.to_csv(\"submission.csv\", index=False)\n","metadata":{"trusted":true,"execution":{"iopub.status.busy":"2025-05-01T10:12:02.234546Z","iopub.execute_input":"2025-05-01T10:12:02.235461Z","iopub.status.idle":"2025-05-01T10:12:13.327955Z","shell.execute_reply.started":"2025-05-01T10:12:02.235425Z","shell.execute_reply":"2025-05-01T10:12:13.326910Z"}},"outputs":[],"execution_count":null},{"cell_type":"markdown","source":"🖼 Computer Vision (CV)\n\nhttps://www.kaggle.com/competitions/dogs-vs-cats\n\nhttps://www.kaggle.com/competitions/facial-keypoints-detection\n\nhttps://www.kaggle.com/competitions/histopathologic-cancer-detection\n\nhttps://www.kaggle.com/competitions/plant-seedlings-classification\n\nhttps://www.kaggle.com/competitions/intel-image-classification\n\n🧠 Natural Language Processing (NLP)\n\nhttps://www.kaggle.com/competitions/nlp-getting-started\n\nhttps://www.kaggle.com/competitions/competitive-data-science-predict-future-sales\n\nhttps://www.kaggle.com/competitions/quora-insincere-questions-classification\n\nhttps://www.kaggle.com/competitions/jigsaw-toxic-comment-classification-challenge\n\nhttps://www.kaggle.com/competitions/tweet-sentiment-extraction\n\n📊 Data Analytics\n\nhttps://www.kaggle.com/competitions/titanic\n\nhttps://www.kaggle.com/competitions/house-prices-advanced-regression-techniques\n\nhttps://www.kaggle.com/competitions/sf-crime\n\nhttps://www.kaggle.com/competitions/zillow-prize-1\n\nhttps://www.kaggle.com/competitions/google-analytics-customer-revenue-prediction\n\n🤖 Classic Machine Learning\n\nhttps://www.kaggle.com/competitions/otto-group-product-classification-challenge\n\nhttps://www.kaggle.com/competitions/bike-sharing-demand\n\nhttps://www.kaggle.com/competitions/forest-cover-type-prediction\n\nhttps://www.kaggle.com/competitions/porto-seguro-safe-driver-prediction\n\nhttps://www.kaggle.com/competitions/santander-customer-satisfaction","metadata":{}},{"cell_type":"markdown","source":"\n---\n\n### 🖼 Computer Vision (CV)\n\n1. **Dogs vs. Cats**\n\n   * Kaggle Notebook: [Dogs vs Cats: Keras Solution](https://www.kaggle.com/code/sarvajna/dogs-vs-cats-keras-solution)\n   * GitHub Repository: [Kaggle-Dogs-vs-cats](https://github.com/chouhbik/Kaggle-Dogs-vs-cats)\n\n2. **Facial Keypoints Detection**\n\n   * Kaggle Notebook: [Facial Keypoint Detection](https://www.kaggle.com/code/karanjakhar/facial-keypoint-detection)\n   * GitHub Repository: [Facial-Keypoint-Detection](https://github.com/Gauravshahare/Facial-Keypoint-Detection-)\n\n3. **Histopathologic Cancer Detection**\n\n   * Kaggle Notebook: [Histopathologic Cancer Detection using CNNs](https://www.kaggle.com/code/abhinand05/histopathologic-cancer-detection-using-cnns)\n   * GitHub Repository: [Kaggle-Histopathologic-Cancer-Detection](https://github.com/23yem/Kaggle-Histopathologic-Cancer-Detection)\n\n4. **Plant Seedlings Classification**\n\n   * Kaggle Notebook: [Plant Seedlings Classification CNN](https://www.kaggle.com/code/xalphahelix/plant-seedlings-classification-cnn)\n   * GitHub Repository: [Plant\\_Seedlings\\_Classification](https://github.com/WuZhuoran/Plant_Seedlings_Classification)\n\n5. **Intel Image Classification**\n\n   * Kaggle Notebook: [Intel Image Classification](https://www.kaggle.com/code/evgenyzorin/intel-image-classification)\n   * GitHub Repository: [intel-image-classification](https://github.com/luangtatipsy/intel-image-classification)\n\n---\n\n### 🧠 Natural Language Processing (NLP)\n\n1. **NLP Getting Started**\n\n   * Kaggle Notebook: [NLP Getting Started Tutorial](https://www.kaggle.com/code/philculliton/nlp-getting-started-tutorial)\n\n2. **Predict Future Sales**\n\n   * Kaggle Notebook: [Predict Future Sales Top 11 Solution](https://www.kaggle.com/code/szhou42/predict-future-sales-top-11-solution)\n   * GitHub Repository: [kaggle-predict-future-sales](https://github.com/nitsaick/kaggle-predict-future-sales)\n\n3. **Quora Insincere Questions Classification**\n\n   * Kaggle Notebook: [Quora Insincere Questions Classification](https://www.kaggle.com/code/obrunet/quora-insincere-questions-classification)\n   * GitHub Repository: [Quora-Insincere-Question-Classification](https://github.com/MohammadWasil/Quora-Insincere-Question-Classification)\n\n4. **Jigsaw Toxic Comment Classification Challenge**\n\n   * GitHub Repository: [Toxic-Comment-Classification-Challenge-NLP](https://github.com/soham97/Toxic-Comment-Classification-Challenge-NLP)\n\n5. **Tweet Sentiment Extraction**\n\n   * Kaggle Notebook: [Tweet Sentiment Extraction Pytorch](https://www.kaggle.com/code/aditidutta/tweet-sentiment-extraction-pytorch)\n   * GitHub Repository: [kaggle\\_tweet](https://github.com/heartkilla/kaggle_tweet)\n\n---\n\n### 📊 Data Analytics\n\n1. **Titanic**\n\n   * Kaggle Notebook: [Top 1% Titanic solution](https://www.kaggle.com/code/nikitakudriashov/top-1-titanic-solution)\n\n2. **House Prices: Advanced Regression Techniques**\n\n   * GitHub Repository: [kaggle-house-prices](https://github.com/PierreExeter/kaggle-house-prices)\n\n3. **San Francisco Crime Classification**\n\n   * Kaggle Notebook: [San Francisco Crime EDA & Classifier](https://www.kaggle.com/code/georgesaavedra/san-francisco-crime-eda-classifier)\n   * GitHub Repository: [kaggle-sf-crime](https://github.com/k-chuang/kaggle-sf-crime)\n\n4. **Zillow Prize: Zillow’s Home Value Prediction (Zestimate)**\n\n   * GitHub Repository: [Zillow-Kaggle](https://github.com/junjiedong/Zillow-Kaggle)\n\n5. **Google Analytics Customer Revenue Prediction**\n\n   * GitHub Repository: [Kaggle\\_Google\\_Analytics](https://github.com/kozodoi/Kaggle_Google_Analytics)\n\n---\n\n### 🤖 Classic Machine Learning\n\n1. **Otto Group Product Classification Challenge**\n\n   * Kaggle Notebook: [Otto Group Product Classification](https://www.kaggle.com/code/ashishpatel26/otto-group-product-classification)\n\n2. **Bike Sharing Demand**\n\n   * Kaggle Notebook: [Bike Sharing Demand](https://www.kaggle.com/code/ashishpatel26/bike-sharing-demand)\n\n3. **Forest Cover Type Prediction**\n\n   * Kaggle Notebook: [Forest Cover Type Prediction](https://www.kaggle.com/code/ashishpatel26/forest-cover-type-prediction)\n\n4. **Porto Seguro’s Safe Driver Prediction**\n\n   * Kaggle Notebook: [Porto Seguro’s Safe Driver Prediction](https://www.kaggle.com/code/ashishpatel26/porto-seguro-s-safe-driver-prediction)\n\n5. **Santander Customer Satisfaction**\n\n   * Kaggle Notebook: [Santander Customer Satisfaction](https://www.kaggle.com/code/ashishpatel26/santander-customer-satisfaction)\n\n---\n\nBatyr, I hope these resources are helpful. If you need further assistance or specific details on any of these solutions, feel free to ask!\n","metadata":{}},{"cell_type":"markdown","source":"Certainly! Here are 20 additional well-known Kaggle competitions across various domains for your practice:\n\n🖼 Computer Vision (CV)\n\t1.\tCarvana Image Masking Challenge\nhttps://www.kaggle.com/competitions/carvana-image-masking-challenge\n\t2.\tiMaterialist Fashion 2019 at FGVC6\nhttps://www.kaggle.com/competitions/imaterialist-fashion-2019-FGVC6\n\t3.\tData Science Bowl 2018\nhttps://www.kaggle.com/competitions/data-science-bowl-2018\n\t4.\tHuman Protein Atlas Image Classification\nhttps://www.kaggle.com/competitions/human-protein-atlas-image-classification\n\t5.\tSIIM-ACR Pneumothorax Segmentation\nhttps://www.kaggle.com/competitions/siim-acr-pneumothorax-segmentation\n\n🧠 Natural Language Processing (NLP)\n\t6.\tToxic Comment Classification Challenge\nhttps://www.kaggle.com/competitions/jigsaw-toxic-comment-classification-challenge\n\t7.\tQuora Question Pairs\nhttps://www.kaggle.com/competitions/quora-question-pairs\n\t8.\tQuora Insincere Questions Classification\nhttps://www.kaggle.com/competitions/quora-insincere-questions-classification\n\t9.\tTweet Sentiment Extraction\nhttps://www.kaggle.com/competitions/tweet-sentiment-extraction\n\t10.\tFeedback Prize - Evaluating Student Writing\nhttps://www.kaggle.com/competitions/feedback-prize-evaluating-student-writing\n\n📊 Data Analytics\n\t11.\tTitanic: Machine Learning from Disaster\nhttps://www.kaggle.com/competitions/titanic\n\t12.\tHouse Prices: Advanced Regression Techniques\nhttps://www.kaggle.com/competitions/house-prices-advanced-regression-techniques\n\t13.\tGoogle Analytics Customer Revenue Prediction\nhttps://www.kaggle.com/competitions/google-analytics-customer-revenue-prediction\n\t14.\tWalmart Recruiting - Store Sales Forecasting\nhttps://www.kaggle.com/competitions/walmart-recruiting-store-sales-forecasting\n\t15.\tRossmann Store Sales\nhttps://www.kaggle.com/competitions/rossmann-store-sales\n\n🤖 Classic Machine Learning\n\t16.\tOtto Group Product Classification Challenge\nhttps://www.kaggle.com/competitions/otto-group-product-classification-challenge\n\t17.\tBike Sharing Demand\nhttps://www.kaggle.com/competitions/bike-sharing-demand\n\t18.\tForest Cover Type Prediction\nhttps://www.kaggle.com/competitions/forest-cover-type-prediction\n\t19.\tSantander Customer Satisfaction\nhttps://www.kaggle.com/competitions/santander-customer-satisfaction\n\t20.\tPorto Seguro’s Safe Driver Prediction\nhttps://www.kaggle.com/competitions/porto-seguro-safe-driver-prediction\n\nThese competitions are excellent for honing your skills in various aspects of data science and machine learning. If you need further assistance or have specific areas you’d like to focus on, feel free to ask!","metadata":{}},{"cell_type":"markdown","source":"Certainly! Here are 20 additional well-known Kaggle competitions across various domains for your practice:\n\n🖼 Computer Vision (CV)\n\t1.\tData Science Bowl 2017\nhttps://www.kaggle.com/competitions/data-science-bowl-2017\n\t2.\tiMaterialist Challenge (Fashion) at FGVC5\nhttps://www.kaggle.com/competitions/imaterialist-challenge-fashion-2018\n\t3.\tSIIM-ISIC Melanoma Classification\nhttps://www.kaggle.com/competitions/siim-isic-melanoma-classification\n\t4.\tRSNA Pneumonia Detection Challenge\nhttps://www.kaggle.com/competitions/rsna-pneumonia-detection-challenge\n\t5.\tGoogle Landmark Recognition 2020\nhttps://www.kaggle.com/competitions/landmark-recognition-2020\n\n🧠 Natural Language Processing (NLP)\n\t6.\tJigsaw Unintended Bias in Toxicity Classification\nhttps://www.kaggle.com/competitions/jigsaw-unintended-bias-in-toxicity-classification\n\t7.\tCommonLit Readability Prize\nhttps://www.kaggle.com/competitions/commonlitreadabilityprize\n\t8.\tTREC Question Classification\nhttps://www.kaggle.com/competitions/trec-question-classification\n\t9.\tDisaster Tweets Classification\nhttps://www.kaggle.com/competitions/nlp-getting-started\n\t10.\tTweet Emotion Recognition\nhttps://www.kaggle.com/competitions/tweet-emotion-recognition\n\n📊 Data Analytics\n\t11.\tWalmart Recruiting - Store Sales Forecasting\nhttps://www.kaggle.com/competitions/walmart-recruiting-store-sales-forecasting\n\t12.\tRossmann Store Sales\nhttps://www.kaggle.com/competitions/rossmann-store-sales\n\t13.\tCorporación Favorita Grocery Sales Forecasting\nhttps://www.kaggle.com/competitions/favorita-grocery-sales-forecasting\n\t14.\tRecruit Restaurant Visitor Forecasting\nhttps://www.kaggle.com/competitions/recruit-restaurant-visitor-forecasting\n\t15.\tWeb Traffic Time Series Forecasting\nhttps://www.kaggle.com/competitions/web-traffic-time-series-forecasting\n\n🤖 Classic Machine Learning\n\t16.\tAllstate Claims Severity\nhttps://www.kaggle.com/competitions/allstate-claims-severity\n\t17.\tSantander Value Prediction Challenge\nhttps://www.kaggle.com/competitions/santander-value-prediction-challenge\n\t18.\tMercedes-Benz Greener Manufacturing\nhttps://www.kaggle.com/competitions/mercedes-benz-greener-manufacturing\n\t19.\tTwo Sigma: Using News to Predict Stock Movements\nhttps://www.kaggle.com/competitions/two-sigma-financial-news\n\t20.\tBNP Paribas Cardif Claims Management\nhttps://www.kaggle.com/competitions/bnp-paribas-cardif-claims-management\n\nThese competitions are excellent for honing your skills in various aspects of data science and machine learning. If you need further assistance or have specific areas you’d like to focus on, feel free to ask!","metadata":{}},{"cell_type":"markdown","source":"Apologies for the earlier repetitions. Here are 20 additional Kaggle competitions across various domains for your practice:\n\n🖼 Computer Vision (CV)\n\t1.\tDigit Recognizer\nhttps://www.kaggle.com/competitions/digit-recognizer\n\t2.\tDiabetic Retinopathy Detection\nhttps://www.kaggle.com/competitions/diabetic-retinopathy-detection\n\t3.\tGalaxy Zoo - The Galaxy Challenge\nhttps://www.kaggle.com/competitions/galaxy-zoo-the-galaxy-challenge\n\t4.\tState Farm Distracted Driver Detection\nhttps://www.kaggle.com/competitions/state-farm-distracted-driver-detection\n\t5.\tNOAA Right Whale Recognition\nhttps://www.kaggle.com/competitions/noaa-right-whale-recognition\n\n🧠 Natural Language Processing (NLP)\n\t6.\tAvito Duplicate Ads Detection\nhttps://www.kaggle.com/competitions/avito-duplicate-ads-detection\n\t7.\tYelp Restaurant Photo Classification\nhttps://www.kaggle.com/competitions/yelp-restaurant-photo-classification\n\t8.\tGender Prediction from Handwriting\nhttps://www.kaggle.com/competitions/icdar2013-gender-prediction-from-handwriting\n\t9.\tMulti-modal Gesture Recognition\nhttps://www.kaggle.com/competitions/multi-modal-gesture-recognition\n\t10.\tIntel & MobileODT Cervical Cancer Screening\nhttps://www.kaggle.com/competitions/intel-mobileodt-cervical-cancer-screening\n\n📊 Data Analytics\n\t11.\tDSTL Satellite Imagery Feature Detection\nhttps://www.kaggle.com/competitions/dstl-satellite-imagery-feature-detection\n\t12.\tNOAA Fisheries Steller Sea Lion Population Count\nhttps://www.kaggle.com/competitions/noaa-fisheries-steller-sea-lion-population-count\n\t13.\tPlanet: Understanding the Amazon from Space\nhttps://www.kaggle.com/competitions/planet-understanding-the-amazon-from-space\n\t14.\tCdiscount’s Image Classification Challenge\nhttps://www.kaggle.com/competitions/cdiscount-image-classification-challenge\n\t15.\tLeaf Classification\nhttps://www.kaggle.com/competitions/leaf-classification\n\n🤖 Classic Machine Learning\n\t16.\tDraper Satellite Image Chronology\nhttps://www.kaggle.com/competitions/draper-satellite-image-chronology\n\t17.\tSecond Annual Data Science Bowl\nhttps://www.kaggle.com/competitions/second-annual-data-science-bowl\n\t18.\tUltrasound Nerve Segmentation\nhttps://www.kaggle.com/competitions/ultrasound-nerve-segmentation\n\t19.\tTGS Salt Identification Challenge\nhttps://www.kaggle.com/competitions/tgs-salt-identification-challenge\n\t20.\tSP Society Camera Model Identification\nhttps://www.kaggle.com/competitions/sp-society-camera-model-identification\n\nThese competitions cover a range of challenges and are excellent for honing your skills in various aspects of data science and machine learning. If you need further assistance or have specific areas you’d like to focus on, feel free to ask!","metadata":{}},{"cell_type":"markdown","source":"Apologies for the earlier repetitions. Here are 20 additional Kaggle competitions across various domains for your practice:\n\n🖼 Computer Vision (CV)\n\t1.\tFacial Keypoints Detection\nhttps://www.kaggle.com/competitions/facial-keypoints-detection\n\t2.\tDog Breed Identification\nhttps://www.kaggle.com/competitions/dog-breed-identification\n\t3.\tHumpback Whale Identification\nhttps://www.kaggle.com/competitions/humpback-whale-identification\n\t4.\tAirbus Ship Detection Challenge\nhttps://www.kaggle.com/competitions/airbus-ship-detection\n\t5.\tSeverstal: Steel Defect Detection\nhttps://www.kaggle.com/competitions/severstal-steel-defect-detection\n\n🧠 Natural Language Processing (NLP)\n\t6.\tJigsaw Multilingual Toxic Comment Classification\nhttps://www.kaggle.com/competitions/jigsaw-multilingual-toxic-comment-classification\n\t7.\tTweet Emotion Recognition\nhttps://www.kaggle.com/competitions/tweet-emotion-recognition\n\t8.\tCommonLit Readability Prize\nhttps://www.kaggle.com/competitions/commonlitreadabilityprize\n\t9.\tFeedback Prize - Evaluating Student Writing\nhttps://www.kaggle.com/competitions/feedback-prize-evaluating-student-writing\n\t10.\tDisaster Tweets Classification\nhttps://www.kaggle.com/competitions/nlp-getting-started\n\n📊 Data Analytics\n\t11.\tCorporación Favorita Grocery Sales Forecasting\nhttps://www.kaggle.com/competitions/favorita-grocery-sales-forecasting\n\t12.\tRecruit Restaurant Visitor Forecasting\nhttps://www.kaggle.com/competitions/recruit-restaurant-visitor-forecasting\n\t13.\tWeb Traffic Time Series Forecasting\nhttps://www.kaggle.com/competitions/web-traffic-time-series-forecasting\n\t14.\tWalmart Recruiting - Store Sales Forecasting\nhttps://www.kaggle.com/competitions/walmart-recruiting-store-sales-forecasting\n\t15.\tRossmann Store Sales\nhttps://www.kaggle.com/competitions/rossmann-store-sales\n\n🤖 Classic Machine Learning\n\t16.\tAllstate Claims Severity\nhttps://www.kaggle.com/competitions/allstate-claims-severity\n\t17.\tSantander Value Prediction Challenge\nhttps://www.kaggle.com/competitions/santander-value-prediction-challenge\n\t18.\tMercedes-Benz Greener Manufacturing\nhttps://www.kaggle.com/competitions/mercedes-benz-greener-manufacturing\n\t19.\tTwo Sigma: Using News to Predict Stock Movements\nhttps://www.kaggle.com/competitions/two-sigma-financial-news\n\t20.\tBNP Paribas Cardif Claims Management\nhttps://www.kaggle.com/competitions/bnp-paribas-cardif-claims-management\n\nThese competitions cover a range of challenges and are excellent for honing your skills in various aspects of data science and machine learning. If you need further assistance or have specific areas you’d like to focus on, feel free to ask!","metadata":{}}]}