![\"Open](\"https://colab.research.google.com/assets/colab-badge.svg\"){kind=icon}
"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {
+ "id": "I1VRs4tZkbvW"
+ },
+ "source": [
+ "# **Day-11 | House price prediction using Linear Regression-SingleVariable**"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {
+ "id": "SAFLqwkKk8rK"
+ },
+ "source": [
+ "### *Import Libraries*"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "metadata": {
+ "id": "EgF2lvr_jzVL"
+ },
+ "source": [
+ "import pandas as pd\n",
+ "from sklearn.linear_model import LinearRegression\n",
+ "import matplotlib.pyplot as plt"
+ ],
+ "execution_count": 1,
+ "outputs": []
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {
+ "id": "XWe_7j6UjxRj"
+ },
+ "source": [
+ "### *Load Dataset from Local Directory*"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "metadata": {
+ "id": "vKrHCJk_jwfJ",
+ "colab": {
+ "base_uri": "https://localhost:8080/",
+ "height": 73
+ },
+ "outputId": "715c67be-afd2-4b0d-c041-a7d3888a5095"
+ },
+ "source": [
+ "from google.colab import files\n",
+ "uploaded = files.upload()"
+ ],
+ "execution_count": 2,
+ "outputs": [
+ {
+ "output_type": "display_data",
+ "data": {
+ "text/plain": [
+ "| \n", + " | area | \n", + "
|---|---|
| 0 | \n", + "8450 | \n", + "
| 1 | \n", + "9600 | \n", + "
| 2 | \n", + "11250 | \n", + "
| 3 | \n", + "9550 | \n", + "
| 4 | \n", + "14260 | \n", + "
| ... | \n", + "... | \n", + "
| 1455 | \n", + "7917 | \n", + "
| 1456 | \n", + "13175 | \n", + "
| 1457 | \n", + "9042 | \n", + "
| 1458 | \n", + "9717 | \n", + "
| 1459 | \n", + "9937 | \n", + "
1460 rows × 1 columns
\n", + "| \n", + " | price | \n", + "
|---|---|
| 0 | \n", + "208500 | \n", + "
| 1 | \n", + "181500 | \n", + "
| 2 | \n", + "223500 | \n", + "
| 3 | \n", + "140000 | \n", + "
| 4 | \n", + "250000 | \n", + "
| ... | \n", + "... | \n", + "
| 1455 | \n", + "175000 | \n", + "
| 1456 | \n", + "210000 | \n", + "
| 1457 | \n", + "266500 | \n", + "
| 1458 | \n", + "142125 | \n", + "
| 1459 | \n", + "147500 | \n", + "
1460 rows × 1 columns
\n", + "LinearRegression()In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook.
LinearRegression()
"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {
+ "id": "I1VRs4tZkbvW"
+ },
+ "source": [
+ "# **Day-12 | Exam mark prediction using Linear Regression-multipleVariable**"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {
+ "id": "SAFLqwkKk8rK"
+ },
+ "source": [
+ "### *Import Libraries*"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "metadata": {
+ "id": "EgF2lvr_jzVL"
+ },
+ "source": [
+ "import pandas as pd\n",
+ "from sklearn.linear_model import LinearRegression"
+ ],
+ "execution_count": 1,
+ "outputs": []
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {
+ "id": "XWe_7j6UjxRj"
+ },
+ "source": [
+ "### *Load Dataset from Local Directory*"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "metadata": {
+ "id": "vKrHCJk_jwfJ",
+ "colab": {
+ "base_uri": "https://localhost:8080/",
+ "height": 73
+ },
+ "outputId": "110e0b18-ab68-4ac6-9e32-733af1698dcc"
+ },
+ "source": [
+ "from google.colab import files\n",
+ "uploaded = files.upload()"
+ ],
+ "execution_count": 2,
+ "outputs": [
+ {
+ "output_type": "display_data",
+ "data": {
+ "text/plain": [
+ "LinearRegression()In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook.
LinearRegression()
"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {
+ "id": "NdHIE4CSDCp3"
+ },
+ "source": [
+ "# **Day-13 | Salary prediction using POLYNOMIAL REGRESSION**"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {
+ "id": "1NTrKL3eIgZ8"
+ },
+ "source": [
+ "### *Importing Libraries*"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "metadata": {
+ "id": "ae6Pxuc-CNeu"
+ },
+ "source": [
+ "import pandas as pd"
+ ],
+ "execution_count": 1,
+ "outputs": []
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {
+ "id": "6oo4HsbHInXM"
+ },
+ "source": [
+ "### *Load Dataset from Local directory*"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "metadata": {
+ "id": "w0WCVounIsJ5",
+ "colab": {
+ "base_uri": "https://localhost:8080/",
+ "height": 73
+ },
+ "outputId": "9bc9721c-cb3d-4eb4-9980-bc718843ae51"
+ },
+ "source": [
+ "from google.colab import files\n",
+ "uploaded = files.upload()"
+ ],
+ "execution_count": 2,
+ "outputs": [
+ {
+ "output_type": "display_data",
+ "data": {
+ "text/plain": [
+ "LinearRegression()In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook.
LinearRegression()
LinearRegression()In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook.
LinearRegression()
"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {
+ "id": "NdHIE4CSDCp3"
+ },
+ "source": [
+ "# **Day-14 | Stock Prediction using SUPPORT VECTOR REGRESSION**"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {
+ "id": "1NTrKL3eIgZ8"
+ },
+ "source": [
+ "### *Importing Libraries*"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "metadata": {
+ "id": "ae6Pxuc-CNeu"
+ },
+ "source": [
+ "import pandas as pd\n",
+ "import numpy as np"
+ ],
+ "execution_count": 1,
+ "outputs": []
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {
+ "id": "6oo4HsbHInXM"
+ },
+ "source": [
+ "### *Load Dataset from Local directory*"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "metadata": {
+ "id": "w0WCVounIsJ5",
+ "colab": {
+ "base_uri": "https://localhost:8080/",
+ "height": 73
+ },
+ "outputId": "72f5c938-c267-41f9-ee2a-6d6563b5664f"
+ },
+ "source": [
+ "from google.colab import files\n",
+ "uploaded = files.upload()"
+ ],
+ "execution_count": 2,
+ "outputs": [
+ {
+ "output_type": "display_data",
+ "data": {
+ "text/plain": [
+ "SVR()In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook.
SVR()
"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {
+ "id": "I1VRs4tZkbvW"
+ },
+ "source": [
+ "# **Day-17 Evaluating Regression ModelUsing RSquared & Adjusted R Squared**"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {
+ "id": "SAFLqwkKk8rK"
+ },
+ "source": [
+ "### *Import Libraries*"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "metadata": {
+ "id": "EgF2lvr_jzVL"
+ },
+ "source": [
+ "import pandas as pd\n",
+ "from sklearn.linear_model import LinearRegression\n",
+ "import matplotlib.pyplot as plt"
+ ],
+ "execution_count": 1,
+ "outputs": []
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {
+ "id": "XWe_7j6UjxRj"
+ },
+ "source": [
+ "### *Load Dataset from Local Directory*"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "metadata": {
+ "id": "vKrHCJk_jwfJ",
+ "colab": {
+ "base_uri": "https://localhost:8080/",
+ "height": 73
+ },
+ "outputId": "f83991a7-4656-4ef5-d91b-b4040836e959"
+ },
+ "source": [
+ "from google.colab import files\n",
+ "uploaded = files.upload()"
+ ],
+ "execution_count": 6,
+ "outputs": [
+ {
+ "output_type": "display_data",
+ "data": {
+ "text/plain": [
+ "| \n", + " | area | \n", + "
|---|---|
| 0 | \n", + "8450 | \n", + "
| 1 | \n", + "9600 | \n", + "
| 2 | \n", + "11250 | \n", + "
| 3 | \n", + "9550 | \n", + "
| 4 | \n", + "14260 | \n", + "
| ... | \n", + "... | \n", + "
| 1455 | \n", + "7917 | \n", + "
| 1456 | \n", + "13175 | \n", + "
| 1457 | \n", + "9042 | \n", + "
| 1458 | \n", + "9717 | \n", + "
| 1459 | \n", + "9937 | \n", + "
1460 rows × 1 columns
\n", + "| \n", + " | area | \n", + "
|---|---|
| 0 | \n", + "8450 | \n", + "
| 1 | \n", + "9600 | \n", + "
| 2 | \n", + "11250 | \n", + "
| 3 | \n", + "9550 | \n", + "
| 4 | \n", + "14260 | \n", + "
| ... | \n", + "... | \n", + "
| 1455 | \n", + "7917 | \n", + "
| 1456 | \n", + "13175 | \n", + "
| 1457 | \n", + "9042 | \n", + "
| 1458 | \n", + "9717 | \n", + "
| 1459 | \n", + "9937 | \n", + "
1460 rows × 1 columns
\n", + "| \n", + " | price | \n", + "
|---|---|
| 0 | \n", + "208500 | \n", + "
| 1 | \n", + "181500 | \n", + "
| 2 | \n", + "223500 | \n", + "
| 3 | \n", + "140000 | \n", + "
| 4 | \n", + "250000 | \n", + "
| ... | \n", + "... | \n", + "
| 1455 | \n", + "175000 | \n", + "
| 1456 | \n", + "210000 | \n", + "
| 1457 | \n", + "266500 | \n", + "
| 1458 | \n", + "142125 | \n", + "
| 1459 | \n", + "147500 | \n", + "
1460 rows × 1 columns
\n", + "LinearRegression()In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook.
LinearRegression()
"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {
+ "id": "r3cas2_1T98w"
+ },
+ "source": [
+ "# Regression Model Selection"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {
+ "id": "IODliia6U1xO"
+ },
+ "source": [
+ "## Importing the basic libraries"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "metadata": {
+ "id": "y98nA5UdU6Hf"
+ },
+ "source": [
+ "import numpy as np\n",
+ "import matplotlib.pyplot as plt\n",
+ "import pandas as pd"
+ ],
+ "execution_count": 1,
+ "outputs": []
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {
+ "id": "2hRC6YEod9_8"
+ },
+ "source": [
+ "### Load Dataset from Local Directory"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "metadata": {
+ "id": "tZBTr4JHeAzb",
+ "colab": {
+ "base_uri": "https://localhost:8080/",
+ "height": 73
+ },
+ "outputId": "c5931337-dd18-4b92-f759-1b45c2bf1a32"
+ },
+ "source": [
+ "from google.colab import files\n",
+ "uploaded = files.upload()"
+ ],
+ "execution_count": 2,
+ "outputs": [
+ {
+ "output_type": "display_data",
+ "data": {
+ "text/plain": [
+ "SVR()In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook.
SVR()
"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {
+ "id": "r3cas2_1T98w"
+ },
+ "source": [
+ "#19 Clusterring Using Income Spent"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {
+ "id": "IODliia6U1xO"
+ },
+ "source": [
+ "## Importing the basic libraries"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "metadata": {
+ "id": "y98nA5UdU6Hf"
+ },
+ "source": [
+ "import pandas as pd\n",
+ "import numpy as np\n",
+ "import matplotlib.pyplot as plt"
+ ],
+ "execution_count": 1,
+ "outputs": []
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {
+ "id": "2hRC6YEod9_8"
+ },
+ "source": [
+ "### Load Dataset from Local Directory"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "metadata": {
+ "id": "tZBTr4JHeAzb",
+ "colab": {
+ "base_uri": "https://localhost:8080/",
+ "height": 73
+ },
+ "outputId": "c555accb-3850-4138-88b9-f770a9cfc1e7"
+ },
+ "source": [
+ "from google.colab import files\n",
+ "uploaded = files.upload()"
+ ],
+ "execution_count": 2,
+ "outputs": [
+ {
+ "output_type": "display_data",
+ "data": {
+ "text/plain": [
+ ""
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {
+ "id": "r3cas2_1T98w"
+ },
+ "source": [
+ "#24_MarketBasketAnalysisusingECLAT"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {
+ "id": "IODliia6U1xO"
+ },
+ "source": [
+ "### Importing the basic libraries"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "metadata": {
+ "id": "y98nA5UdU6Hf"
+ },
+ "source": [
+ "import numpy as np\n",
+ "import pandas as pd\n",
+ "import matplotlib.pyplot as plt"
+ ],
+ "execution_count": 1,
+ "outputs": []
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {
+ "id": "jpjZ43YlU8eI"
+ },
+ "source": [
+ "### Load Dataset from Local Directory"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "metadata": {
+ "id": "pLVaXoYVU_Uy",
+ "colab": {
+ "base_uri": "https://localhost:8080/",
+ "height": 73
+ },
+ "outputId": "f4f73900-25f5-457e-e238-8d490bcb8c44"
+ },
+ "source": [
+ "from google.colab import files\n",
+ "uploaded = files.upload()"
+ ],
+ "execution_count": 2,
+ "outputs": [
+ {
+ "output_type": "display_data",
+ "data": {
+ "text/plain": [
+ "| \n", + " | Left Hand Side | \n", + "Right Hand Side | \n", + "Support | \n", + "
|---|---|---|---|
| 0 | \n", + "body spray | \n", + "avocado | \n", + "0.014286 | \n", + "
| 1 | \n", + "brownies | \n", + "avocado | \n", + "0.014286 | \n", + "
| 2 | \n", + "cider | \n", + "avocado | \n", + "0.014286 | \n", + "
| 3 | \n", + "fresh bread | \n", + "avocado | \n", + "0.014286 | \n", + "
| 4 | \n", + "grated cheese | \n", + "avocado | \n", + "0.014286 | \n", + "
| ... | \n", + "... | \n", + "... | \n", + "... | \n", + "
| 271 | \n", + "strawberries | \n", + "turkey | \n", + "0.014286 | \n", + "
| 272 | \n", + "tomato sauce | \n", + "tomatoes | \n", + "0.014286 | \n", + "
| 273 | \n", + "tomatoes | \n", + "turkey | \n", + "0.028571 | \n", + "
| 274 | \n", + "toothpaste | \n", + "white wine | \n", + "0.014286 | \n", + "
| 275 | \n", + "whole wheat pasta | \n", + "whole wheat rice | \n", + "0.014286 | \n", + "
276 rows × 3 columns
\n", + "| \n", + " | Left Hand Side | \n", + "Right Hand Side | \n", + "Support | \n", + "
|---|---|---|---|
| 146 | \n", + "fresh tuna | \n", + "mineral water | \n", + "0.057143 | \n", + "
| 186 | \n", + "ground beef | \n", + "mineral water | \n", + "0.057143 | \n", + "
| 29 | \n", + "body spray | \n", + "green tea | \n", + "0.042857 | \n", + "
| 149 | \n", + "fresh tuna | \n", + "spaghetti | \n", + "0.042857 | \n", + "
| 182 | \n", + "green tea | \n", + "soup | \n", + "0.042857 | \n", + "
| 236 | \n", + "whole wheat rice | \n", + "mineral water | \n", + "0.042857 | \n", + "
| 249 | \n", + "pasta | \n", + "shrimp | \n", + "0.042857 | \n", + "
| 261 | \n", + "salmon | \n", + "spaghetti | \n", + "0.042857 | \n", + "
| 24 | \n", + "black tea | \n", + "salmon | \n", + "0.028571 | \n", + "
| 33 | \n", + "body spray | \n", + "pancakes | \n", + "0.028571 | \n", + "
"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {
+ "id": "r3cas2_1T98w"
+ },
+ "source": [
+ "#25_WebAdOptimization_UpperConfidenceBound_ReinforcementLearning"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {
+ "id": "IODliia6U1xO"
+ },
+ "source": [
+ "### Importing the basic libraries"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "metadata": {
+ "id": "y98nA5UdU6Hf"
+ },
+ "source": [
+ "import numpy as np\n",
+ "import pandas as pd\n",
+ "import matplotlib.pyplot as plt"
+ ],
+ "execution_count": 1,
+ "outputs": []
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {
+ "id": "jpjZ43YlU8eI"
+ },
+ "source": [
+ "### Load Dataset from Local Directory"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "metadata": {
+ "id": "pLVaXoYVU_Uy",
+ "colab": {
+ "base_uri": "https://localhost:8080/",
+ "height": 73
+ },
+ "outputId": "1e35d194-6168-4329-e254-b70e50cca5c6"
+ },
+ "source": [
+ "from google.colab import files\n",
+ "uploaded = files.upload()"
+ ],
+ "execution_count": 2,
+ "outputs": [
+ {
+ "output_type": "display_data",
+ "data": {
+ "text/plain": [
+ ""
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {
+ "id": "Mkgh9SoBSOwH"
+ },
+ "source": [
+ "#26 Sentimental Analysis NLP"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "metadata": {
+ "id": "_UNRNEQQG9Sr",
+ "colab": {
+ "base_uri": "https://localhost:8080/"
+ },
+ "outputId": "be97a8fd-45a4-4ff2-d743-ad5ff1a29cf0"
+ },
+ "source": [
+ "!pip install nltk"
+ ],
+ "execution_count": 1,
+ "outputs": [
+ {
+ "output_type": "stream",
+ "name": "stdout",
+ "text": [
+ "Requirement already satisfied: nltk in /usr/local/lib/python3.11/dist-packages (3.9.1)\n",
+ "Requirement already satisfied: click in /usr/local/lib/python3.11/dist-packages (from nltk) (8.1.8)\n",
+ "Requirement already satisfied: joblib in /usr/local/lib/python3.11/dist-packages (from nltk) (1.4.2)\n",
+ "Requirement already satisfied: regex>=2021.8.3 in /usr/local/lib/python3.11/dist-packages (from nltk) (2024.11.6)\n",
+ "Requirement already satisfied: tqdm in /usr/local/lib/python3.11/dist-packages (from nltk) (4.67.1)\n"
+ ]
+ }
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {
+ "id": "v6-o7BjJTzVx"
+ },
+ "source": [
+ "### Importing Libraries"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "metadata": {
+ "id": "TLZbzW6wT2ZQ"
+ },
+ "source": [
+ "import numpy as np\n",
+ "import pandas as pd\n",
+ "import re #Regular expressions\n",
+ "import nltk\n",
+ "import matplotlib.pyplot as plt\n",
+ "\n",
+ "from nltk.corpus import stopwords\n",
+ "\n",
+ "from sklearn.feature_extraction.text import TfidfVectorizer\n",
+ "from sklearn.ensemble import RandomForestClassifier\n",
+ "from sklearn.metrics import accuracy_score\n",
+ "\n",
+ "from sklearn.model_selection import train_test_split"
+ ],
+ "execution_count": 2,
+ "outputs": []
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {
+ "id": "NkPr6C5_UH9d"
+ },
+ "source": [
+ "### Load Dataset from Local Directory"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "metadata": {
+ "id": "F5rC2iqzUJEm",
+ "colab": {
+ "base_uri": "https://localhost:8080/",
+ "height": 73
+ },
+ "outputId": "00a19f35-0083-435f-8a27-b21c6b2dd1d7"
+ },
+ "source": [
+ "from google.colab import files\n",
+ "uploaded = files.upload()"
+ ],
+ "execution_count": 3,
+ "outputs": [
+ {
+ "output_type": "display_data",
+ "data": {
+ "text/plain": [
+ "RandomForestClassifier(n_estimators=200, random_state=0)In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook.
RandomForestClassifier(n_estimators=200, random_state=0)
"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {
+ "id": "AhpeX5Dm_eLW"
+ },
+ "source": [
+ "# **Day-10_BreastCancerDetection_VariousMLAlgorithm**"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {
+ "id": "8rvHswu2td0Z"
+ },
+ "source": [
+ "### *Importing Libraries*"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "metadata": {
+ "id": "P-YXxKhu_Uk4"
+ },
+ "source": [
+ "import pandas as pd #useful for loading the dataset\n",
+ "import numpy as np #to perform array\n",
+ "from matplotlib import pyplot"
+ ],
+ "execution_count": 1,
+ "outputs": []
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {
+ "id": "mOt3nocbwvZk"
+ },
+ "source": [
+ "### *Choose Dataset from Local Directory*"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "metadata": {
+ "id": "xB2NMwXtw2dG",
+ "colab": {
+ "base_uri": "https://localhost:8080/",
+ "height": 73
+ },
+ "outputId": "ec354929-3ba9-42a1-b9bb-ccf832a4bb1b"
+ },
+ "source": [
+ "from google.colab import files\n",
+ "uploaded = files.upload()"
+ ],
+ "execution_count": 2,
+ "outputs": [
+ {
+ "output_type": "display_data",
+ "data": {
+ "text/plain": [
+ ""
+ ]
+ },
+ {
+ "cell_type": "code",
+ "source": [
+ "import numpy as np\n",
+ "import pandas as pd\n",
+ "import tensorflow as tf\n",
+ "import matplotlib.pyplot as plt\n",
+ "from tensorflow.keras.models import Sequential\n",
+ "from tensorflow.keras.layers import LSTM, Dense\n",
+ "from sklearn.model_selection import train_test_split\n",
+ "from sklearn.preprocessing import StandardScaler\n",
+ "\n",
+ "# Load dataset from CSV file\n",
+ "def load_rpm_data(file_path):\n",
+ " df = pd.read_csv('rpm_deceleration_dataset.csv')\n",
+ " X = df.iloc[:, :-1].values # Features (RPM data sequences)\n",
+ " y = df.iloc[:, -1].values # Labels (1 for deceleration, 0 otherwise)\n",
+ " return X, y\n",
+ "\n",
+ "# File path to dataset\n",
+ "file_path = r\"E:\\Decceleration_analysisof_athelete using LSTM real world project6\\rpm_deceleration_dataset.csv\"\n",
+ "\n",
+ "# Prepare dataset\n",
+ "X, y = load_rpm_data(file_path)\n",
+ "scaler = StandardScaler()\n",
+ "X = scaler.fit_transform(X)\n",
+ "X = X.reshape(X.shape[0], X.shape[1], 1) # Reshape for LSTM\n",
+ "\n",
+ "# Train-test split\n",
+ "X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=42)\n",
+ "\n",
+ "# Define LSTM model\n",
+ "model = Sequential([\n",
+ " LSTM(50, return_sequences=False, input_shape=(X_train.shape[1], 1)),\n",
+ " Dense(1, activation='sigmoid')\n",
+ "])\n",
+ "\n",
+ "# Compile model\n",
+ "model.compile(optimizer='adam', loss='binary_crossentropy', metrics=['accuracy'])\n",
+ "\n",
+ "# Train model\n",
+ "model.fit(X_train, y_train, epochs=20, batch_size=16, validation_data=(X_test, y_test))\n",
+ "\n",
+ "# Evaluate model\n",
+ "test_loss, test_accuracy = model.evaluate(X_test, y_test)\n",
+ "print(f\"Test Accuracy: {test_accuracy * 100:.2f}%\")\n",
+ "\n",
+ "# Plot histogram for deceleration per minute\n",
+ "plt.hist(y, bins=10, edgecolor='black')\n",
+ "plt.xlabel(\"Deceleration per Minute\")\n",
+ "plt.ylabel(\"Frequency\")\n",
+ "plt.title(\"Histogram of Deceleration per Minute\")\n",
+ "plt.show()\n"
+ ],
+ "metadata": {
+ "colab": {
+ "base_uri": "https://localhost:8080/",
+ "height": 1000
+ },
+ "id": "UUZslFlZkBpN",
+ "outputId": "bc7782c9-d45b-4f0f-bdf5-51f021355a4d"
+ },
+ "execution_count": 25,
+ "outputs": [
+ {
+ "output_type": "stream",
+ "name": "stdout",
+ "text": [
+ "Epoch 1/20\n"
+ ]
+ },
+ {
+ "output_type": "stream",
+ "name": "stderr",
+ "text": [
+ "/usr/local/lib/python3.11/dist-packages/keras/src/layers/rnn/rnn.py:200: UserWarning: Do not pass an `input_shape`/`input_dim` argument to a layer. When using Sequential models, prefer using an `Input(shape)` object as the first layer in the model instead.\n",
+ " super().__init__(**kwargs)\n"
+ ]
+ },
+ {
+ "output_type": "stream",
+ "name": "stdout",
+ "text": [
+ "\u001b[1m250/250\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m4s\u001b[0m 8ms/step - accuracy: 0.6656 - loss: 0.6481 - val_accuracy: 0.6670 - val_loss: 0.6271\n",
+ "Epoch 2/20\n",
+ "\u001b[1m250/250\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 6ms/step - accuracy: 0.6743 - loss: 0.6228 - val_accuracy: 0.6670 - val_loss: 0.6166\n",
+ "Epoch 3/20\n",
+ "\u001b[1m250/250\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 6ms/step - accuracy: 0.6601 - loss: 0.6213 - val_accuracy: 0.6820 - val_loss: 0.5975\n",
+ "Epoch 4/20\n",
+ "\u001b[1m250/250\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 6ms/step - accuracy: 0.6579 - loss: 0.6192 - val_accuracy: 0.6800 - val_loss: 0.5720\n",
+ "Epoch 5/20\n",
+ "\u001b[1m250/250\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 6ms/step - accuracy: 0.6803 - loss: 0.5896 - val_accuracy: 0.6830 - val_loss: 0.5519\n",
+ "Epoch 6/20\n",
+ "\u001b[1m250/250\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m3s\u001b[0m 7ms/step - accuracy: 0.6937 - loss: 0.5628 - val_accuracy: 0.6500 - val_loss: 0.5887\n",
+ "Epoch 7/20\n",
+ "\u001b[1m250/250\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 6ms/step - accuracy: 0.7021 - loss: 0.5405 - val_accuracy: 0.7390 - val_loss: 0.4979\n",
+ "Epoch 8/20\n",
+ "\u001b[1m250/250\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 6ms/step - accuracy: 0.7465 - loss: 0.4933 - val_accuracy: 0.7320 - val_loss: 0.5215\n",
+ "Epoch 9/20\n",
+ "\u001b[1m250/250\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 6ms/step - accuracy: 0.7660 - loss: 0.4742 - val_accuracy: 0.7850 - val_loss: 0.4273\n",
+ "Epoch 10/20\n",
+ "\u001b[1m250/250\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 7ms/step - accuracy: 0.8209 - loss: 0.3972 - val_accuracy: 0.8270 - val_loss: 0.3901\n",
+ "Epoch 11/20\n",
+ "\u001b[1m250/250\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 6ms/step - accuracy: 0.8213 - loss: 0.3796 - val_accuracy: 0.8420 - val_loss: 0.3595\n",
+ "Epoch 12/20\n",
+ "\u001b[1m250/250\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 7ms/step - accuracy: 0.8408 - loss: 0.3634 - val_accuracy: 0.7950 - val_loss: 0.4218\n",
+ "Epoch 13/20\n",
+ "\u001b[1m250/250\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 9ms/step - accuracy: 0.8401 - loss: 0.3503 - val_accuracy: 0.8330 - val_loss: 0.3686\n",
+ "Epoch 14/20\n",
+ "\u001b[1m250/250\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 6ms/step - accuracy: 0.8424 - loss: 0.3528 - val_accuracy: 0.8220 - val_loss: 0.3770\n",
+ "Epoch 15/20\n",
+ "\u001b[1m250/250\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 6ms/step - accuracy: 0.8564 - loss: 0.3298 - val_accuracy: 0.8210 - val_loss: 0.4038\n",
+ "Epoch 16/20\n",
+ "\u001b[1m250/250\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m3s\u001b[0m 7ms/step - accuracy: 0.8446 - loss: 0.3439 - val_accuracy: 0.8730 - val_loss: 0.3035\n",
+ "Epoch 17/20\n",
+ "\u001b[1m250/250\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 6ms/step - accuracy: 0.8501 - loss: 0.3258 - val_accuracy: 0.8690 - val_loss: 0.3123\n",
+ "Epoch 18/20\n",
+ "\u001b[1m250/250\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m3s\u001b[0m 9ms/step - accuracy: 0.8560 - loss: 0.3279 - val_accuracy: 0.8600 - val_loss: 0.3087\n",
+ "Epoch 19/20\n",
+ "\u001b[1m250/250\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 7ms/step - accuracy: 0.8530 - loss: 0.3319 - val_accuracy: 0.8550 - val_loss: 0.3229\n",
+ "Epoch 20/20\n",
+ "\u001b[1m250/250\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 7ms/step - accuracy: 0.8545 - loss: 0.3269 - val_accuracy: 0.8500 - val_loss: 0.3279\n",
+ "\u001b[1m32/32\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 4ms/step - accuracy: 0.8555 - loss: 0.3230\n",
+ "Test Accuracy: 85.00%\n"
+ ]
+ },
+ {
+ "output_type": "display_data",
+ "data": {
+ "text/plain": [
+ ""
+ ]
+ },
+ {
+ "cell_type": "code",
+ "source": [
+ "pip install numpy scipy matplotlib mrcfile scikit-image\n"
+ ],
+ "metadata": {
+ "colab": {
+ "base_uri": "https://localhost:8080/"
+ },
+ "id": "VtHNWFURhnKX",
+ "outputId": "3c55cad3-2f4a-4aec-81c9-06365d58c9a7"
+ },
+ "execution_count": 20,
+ "outputs": [
+ {
+ "output_type": "stream",
+ "name": "stdout",
+ "text": [
+ "Requirement already satisfied: numpy in /usr/local/lib/python3.11/dist-packages (1.26.4)\n",
+ "Requirement already satisfied: scipy in /usr/local/lib/python3.11/dist-packages (1.13.1)\n",
+ "Requirement already satisfied: matplotlib in /usr/local/lib/python3.11/dist-packages (3.10.0)\n",
+ "Requirement already satisfied: mrcfile in /usr/local/lib/python3.11/dist-packages (1.5.4)\n",
+ "Requirement already satisfied: scikit-image in /usr/local/lib/python3.11/dist-packages (0.25.2)\n",
+ "Requirement already satisfied: contourpy>=1.0.1 in /usr/local/lib/python3.11/dist-packages (from matplotlib) (1.3.1)\n",
+ "Requirement already satisfied: cycler>=0.10 in /usr/local/lib/python3.11/dist-packages (from matplotlib) (0.12.1)\n",
+ "Requirement already satisfied: fonttools>=4.22.0 in /usr/local/lib/python3.11/dist-packages (from matplotlib) (4.56.0)\n",
+ "Requirement already satisfied: kiwisolver>=1.3.1 in /usr/local/lib/python3.11/dist-packages (from matplotlib) (1.4.8)\n",
+ "Requirement already satisfied: packaging>=20.0 in /usr/local/lib/python3.11/dist-packages (from matplotlib) (24.2)\n",
+ "Requirement already satisfied: pillow>=8 in /usr/local/lib/python3.11/dist-packages (from matplotlib) (11.1.0)\n",
+ "Requirement already satisfied: pyparsing>=2.3.1 in /usr/local/lib/python3.11/dist-packages (from matplotlib) (3.2.1)\n",
+ "Requirement already satisfied: python-dateutil>=2.7 in /usr/local/lib/python3.11/dist-packages (from matplotlib) (2.8.2)\n",
+ "Requirement already satisfied: networkx>=3.0 in /usr/local/lib/python3.11/dist-packages (from scikit-image) (3.4.2)\n",
+ "Requirement already satisfied: imageio!=2.35.0,>=2.33 in /usr/local/lib/python3.11/dist-packages (from scikit-image) (2.37.0)\n",
+ "Requirement already satisfied: tifffile>=2022.8.12 in /usr/local/lib/python3.11/dist-packages (from scikit-image) (2025.2.18)\n",
+ "Requirement already satisfied: lazy-loader>=0.4 in /usr/local/lib/python3.11/dist-packages (from scikit-image) (0.4)\n",
+ "Requirement already satisfied: six>=1.5 in /usr/local/lib/python3.11/dist-packages (from python-dateutil>=2.7->matplotlib) (1.17.0)\n"
+ ]
+ }
+ ]
+ },
+ {
+ "cell_type": "code",
+ "source": [
+ "import requests\n",
+ "import gzip\n",
+ "import shutil\n",
+ "import mrcfile\n",
+ "import matplotlib.pyplot as plt\n",
+ "\n",
+ "# Step 1: Download the dataset\n",
+ "url = 'https://ftp.ebi.ac.uk/pub/databases/emdb/structures/EMD-0465/map/emd_0465.map.gz'\n",
+ "response = requests.get(url)\n",
+ "if response.status_code == 200:\n",
+ " with open('emd_0465.map.gz', 'wb') as file:\n",
+ " file.write(response.content)\n",
+ "else:\n",
+ " print(f\"Failed to download the file. Status code: {response.status_code}\")\n",
+ "\n",
+ "# Step 2: Extract the .gz file\n",
+ "with gzip.open('emd_0465.map.gz', 'rb') as f_in:\n",
+ " with open('emd_0465.map', 'wb') as f_out:\n",
+ " shutil.copyfileobj(f_in, f_out)\n",
+ "\n",
+ "# Step 3: Load the map file\n",
+ "with mrcfile.open('emd_0465.map', permissive=True) as mrc:\n",
+ " volume = mrc.data\n",
+ " if volume is None:\n",
+ " raise ValueError(\"Failed to load volume data from the map file.\")\n",
+ "\n",
+ "# Step 4: Visualize the middle slice\n",
+ "if volume is not None:\n",
+ " plt.imshow(volume[volume.shape[0] // 2], cmap='gray')\n",
+ " plt.title('Middle Slice of EMD-0465')\n",
+ " plt.show()\n",
+ "else:\n",
+ " print(\"Volume data is None. Cannot visualize.\")\n"
+ ],
+ "metadata": {
+ "colab": {
+ "base_uri": "https://localhost:8080/",
+ "height": 452
+ },
+ "id": "44dkua4UjdeG",
+ "outputId": "d215ed13-f34d-4247-b3df-92425f02eab7"
+ },
+ "execution_count": 21,
+ "outputs": [
+ {
+ "output_type": "display_data",
+ "data": {
+ "text/plain": [
+ ""
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {
+ "id": "l-FmNW1v-wEx"
+ },
+ "source": [
+ "#Handwritten Digit Recognition | SVM**"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {
+ "id": "aPZ-cSea-8Pz"
+ },
+ "source": [
+ "### *Importing Basic Libraries*"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "metadata": {
+ "id": "KgOTIpQe-Sij"
+ },
+ "source": [
+ "import numpy as np\n",
+ "from sklearn.datasets import load_digits"
+ ],
+ "execution_count": 1,
+ "outputs": []
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {
+ "id": "tAEv1LgB_OxV"
+ },
+ "source": [
+ "### *Load Dataset*\n",
+ ""
+ ]
+ },
+ {
+ "cell_type": "code",
+ "metadata": {
+ "id": "mEkoGFM3_Rl6"
+ },
+ "source": [
+ "dataset = load_digits()"
+ ],
+ "execution_count": 2,
+ "outputs": []
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {
+ "id": "fIw9RCUrACG9"
+ },
+ "source": [
+ "### *Summarize Dataset*"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "metadata": {
+ "id": "OKcoy2EJAFj2",
+ "colab": {
+ "base_uri": "https://localhost:8080/"
+ },
+ "outputId": "6ca48cd3-8140-4383-f915-8586948f2b80"
+ },
+ "source": [
+ "print(dataset.data)\n",
+ "print(dataset.target)\n",
+ "\n",
+ "print(dataset.data.shape)\n",
+ "print(dataset.images.shape)\n",
+ "\n",
+ "dataimageLength = len(dataset.images)\n",
+ "print(dataimageLength)"
+ ],
+ "execution_count": 3,
+ "outputs": [
+ {
+ "output_type": "stream",
+ "name": "stdout",
+ "text": [
+ "[[ 0. 0. 5. ... 0. 0. 0.]\n",
+ " [ 0. 0. 0. ... 10. 0. 0.]\n",
+ " [ 0. 0. 0. ... 16. 9. 0.]\n",
+ " ...\n",
+ " [ 0. 0. 1. ... 6. 0. 0.]\n",
+ " [ 0. 0. 2. ... 12. 0. 0.]\n",
+ " [ 0. 0. 10. ... 12. 1. 0.]]\n",
+ "[0 1 2 ... 8 9 8]\n",
+ "(1797, 64)\n",
+ "(1797, 8, 8)\n",
+ "1797\n"
+ ]
+ }
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {
+ "id": "epmSBzRtCgdh"
+ },
+ "source": [
+ "### *Visualize the Dataset*"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "metadata": {
+ "id": "sGYJfmAvCj3a",
+ "colab": {
+ "base_uri": "https://localhost:8080/",
+ "height": 593
+ },
+ "outputId": "992865b4-a1ad-4ebd-91c9-1918361c48b6"
+ },
+ "source": [
+ "n=7 #No. of Sample out of Samples total 1797\n",
+ "\n",
+ "import matplotlib.pyplot as plt\n",
+ "plt.gray()\n",
+ "plt.matshow(dataset.images[n])\n",
+ "plt.show()\n",
+ "\n",
+ "dataset.images[n]"
+ ],
+ "execution_count": 5,
+ "outputs": [
+ {
+ "output_type": "display_data",
+ "data": {
+ "text/plain": [
+ "SVC(kernel='linear')In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook.
SVC(kernel='linear')
"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {
+ "id": "aYYh8N1KOChK"
+ },
+ "source": [
+ "# **Day-5 | Leaf Species Detection | DECISION TREE**"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {
+ "id": "TUcGB8gXNufD"
+ },
+ "source": [
+ "### *Import basic Libraries*"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "metadata": {
+ "id": "XBeYR9xuNn_1"
+ },
+ "source": [
+ "from sklearn.datasets import load_iris\n",
+ "import pandas as pd\n",
+ "import numpy as np"
+ ],
+ "execution_count": 1,
+ "outputs": []
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {
+ "id": "RSWsaAz7OOzj"
+ },
+ "source": [
+ "### *Load Dataset*"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "metadata": {
+ "id": "1gYg7y7WOai_"
+ },
+ "source": [
+ "dataset = load_iris()"
+ ],
+ "execution_count": 2,
+ "outputs": []
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {
+ "id": "ql3v6_gZOjAD"
+ },
+ "source": [
+ "### *Summarize Dataset*"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "metadata": {
+ "colab": {
+ "base_uri": "https://localhost:8080/"
+ },
+ "id": "_x9QzbrMOl0l",
+ "outputId": "ab81e859-9cc0-4483-c174-5012b2b47ca4"
+ },
+ "source": [
+ "print(dataset.data)\n",
+ "print(dataset.target)\n",
+ "\n",
+ "print(dataset.data.shape)"
+ ],
+ "execution_count": 3,
+ "outputs": [
+ {
+ "output_type": "stream",
+ "name": "stdout",
+ "text": [
+ "[[5.1 3.5 1.4 0.2]\n",
+ " [4.9 3. 1.4 0.2]\n",
+ " [4.7 3.2 1.3 0.2]\n",
+ " [4.6 3.1 1.5 0.2]\n",
+ " [5. 3.6 1.4 0.2]\n",
+ " [5.4 3.9 1.7 0.4]\n",
+ " [4.6 3.4 1.4 0.3]\n",
+ " [5. 3.4 1.5 0.2]\n",
+ " [4.4 2.9 1.4 0.2]\n",
+ " [4.9 3.1 1.5 0.1]\n",
+ " [5.4 3.7 1.5 0.2]\n",
+ " [4.8 3.4 1.6 0.2]\n",
+ " [4.8 3. 1.4 0.1]\n",
+ " [4.3 3. 1.1 0.1]\n",
+ " [5.8 4. 1.2 0.2]\n",
+ " [5.7 4.4 1.5 0.4]\n",
+ " [5.4 3.9 1.3 0.4]\n",
+ " [5.1 3.5 1.4 0.3]\n",
+ " [5.7 3.8 1.7 0.3]\n",
+ " [5.1 3.8 1.5 0.3]\n",
+ " [5.4 3.4 1.7 0.2]\n",
+ " [5.1 3.7 1.5 0.4]\n",
+ " [4.6 3.6 1. 0.2]\n",
+ " [5.1 3.3 1.7 0.5]\n",
+ " [4.8 3.4 1.9 0.2]\n",
+ " [5. 3. 1.6 0.2]\n",
+ " [5. 3.4 1.6 0.4]\n",
+ " [5.2 3.5 1.5 0.2]\n",
+ " [5.2 3.4 1.4 0.2]\n",
+ " [4.7 3.2 1.6 0.2]\n",
+ " [4.8 3.1 1.6 0.2]\n",
+ " [5.4 3.4 1.5 0.4]\n",
+ " [5.2 4.1 1.5 0.1]\n",
+ " [5.5 4.2 1.4 0.2]\n",
+ " [4.9 3.1 1.5 0.2]\n",
+ " [5. 3.2 1.2 0.2]\n",
+ " [5.5 3.5 1.3 0.2]\n",
+ " [4.9 3.6 1.4 0.1]\n",
+ " [4.4 3. 1.3 0.2]\n",
+ " [5.1 3.4 1.5 0.2]\n",
+ " [5. 3.5 1.3 0.3]\n",
+ " [4.5 2.3 1.3 0.3]\n",
+ " [4.4 3.2 1.3 0.2]\n",
+ " [5. 3.5 1.6 0.6]\n",
+ " [5.1 3.8 1.9 0.4]\n",
+ " [4.8 3. 1.4 0.3]\n",
+ " [5.1 3.8 1.6 0.2]\n",
+ " [4.6 3.2 1.4 0.2]\n",
+ " [5.3 3.7 1.5 0.2]\n",
+ " [5. 3.3 1.4 0.2]\n",
+ " [7. 3.2 4.7 1.4]\n",
+ " [6.4 3.2 4.5 1.5]\n",
+ " [6.9 3.1 4.9 1.5]\n",
+ " [5.5 2.3 4. 1.3]\n",
+ " [6.5 2.8 4.6 1.5]\n",
+ " [5.7 2.8 4.5 1.3]\n",
+ " [6.3 3.3 4.7 1.6]\n",
+ " [4.9 2.4 3.3 1. ]\n",
+ " [6.6 2.9 4.6 1.3]\n",
+ " [5.2 2.7 3.9 1.4]\n",
+ " [5. 2. 3.5 1. ]\n",
+ " [5.9 3. 4.2 1.5]\n",
+ " [6. 2.2 4. 1. ]\n",
+ " [6.1 2.9 4.7 1.4]\n",
+ " [5.6 2.9 3.6 1.3]\n",
+ " [6.7 3.1 4.4 1.4]\n",
+ " [5.6 3. 4.5 1.5]\n",
+ " [5.8 2.7 4.1 1. ]\n",
+ " [6.2 2.2 4.5 1.5]\n",
+ " [5.6 2.5 3.9 1.1]\n",
+ " [5.9 3.2 4.8 1.8]\n",
+ " [6.1 2.8 4. 1.3]\n",
+ " [6.3 2.5 4.9 1.5]\n",
+ " [6.1 2.8 4.7 1.2]\n",
+ " [6.4 2.9 4.3 1.3]\n",
+ " [6.6 3. 4.4 1.4]\n",
+ " [6.8 2.8 4.8 1.4]\n",
+ " [6.7 3. 5. 1.7]\n",
+ " [6. 2.9 4.5 1.5]\n",
+ " [5.7 2.6 3.5 1. ]\n",
+ " [5.5 2.4 3.8 1.1]\n",
+ " [5.5 2.4 3.7 1. ]\n",
+ " [5.8 2.7 3.9 1.2]\n",
+ " [6. 2.7 5.1 1.6]\n",
+ " [5.4 3. 4.5 1.5]\n",
+ " [6. 3.4 4.5 1.6]\n",
+ " [6.7 3.1 4.7 1.5]\n",
+ " [6.3 2.3 4.4 1.3]\n",
+ " [5.6 3. 4.1 1.3]\n",
+ " [5.5 2.5 4. 1.3]\n",
+ " [5.5 2.6 4.4 1.2]\n",
+ " [6.1 3. 4.6 1.4]\n",
+ " [5.8 2.6 4. 1.2]\n",
+ " [5. 2.3 3.3 1. ]\n",
+ " [5.6 2.7 4.2 1.3]\n",
+ " [5.7 3. 4.2 1.2]\n",
+ " [5.7 2.9 4.2 1.3]\n",
+ " [6.2 2.9 4.3 1.3]\n",
+ " [5.1 2.5 3. 1.1]\n",
+ " [5.7 2.8 4.1 1.3]\n",
+ " [6.3 3.3 6. 2.5]\n",
+ " [5.8 2.7 5.1 1.9]\n",
+ " [7.1 3. 5.9 2.1]\n",
+ " [6.3 2.9 5.6 1.8]\n",
+ " [6.5 3. 5.8 2.2]\n",
+ " [7.6 3. 6.6 2.1]\n",
+ " [4.9 2.5 4.5 1.7]\n",
+ " [7.3 2.9 6.3 1.8]\n",
+ " [6.7 2.5 5.8 1.8]\n",
+ " [7.2 3.6 6.1 2.5]\n",
+ " [6.5 3.2 5.1 2. ]\n",
+ " [6.4 2.7 5.3 1.9]\n",
+ " [6.8 3. 5.5 2.1]\n",
+ " [5.7 2.5 5. 2. ]\n",
+ " [5.8 2.8 5.1 2.4]\n",
+ " [6.4 3.2 5.3 2.3]\n",
+ " [6.5 3. 5.5 1.8]\n",
+ " [7.7 3.8 6.7 2.2]\n",
+ " [7.7 2.6 6.9 2.3]\n",
+ " [6. 2.2 5. 1.5]\n",
+ " [6.9 3.2 5.7 2.3]\n",
+ " [5.6 2.8 4.9 2. ]\n",
+ " [7.7 2.8 6.7 2. ]\n",
+ " [6.3 2.7 4.9 1.8]\n",
+ " [6.7 3.3 5.7 2.1]\n",
+ " [7.2 3.2 6. 1.8]\n",
+ " [6.2 2.8 4.8 1.8]\n",
+ " [6.1 3. 4.9 1.8]\n",
+ " [6.4 2.8 5.6 2.1]\n",
+ " [7.2 3. 5.8 1.6]\n",
+ " [7.4 2.8 6.1 1.9]\n",
+ " [7.9 3.8 6.4 2. ]\n",
+ " [6.4 2.8 5.6 2.2]\n",
+ " [6.3 2.8 5.1 1.5]\n",
+ " [6.1 2.6 5.6 1.4]\n",
+ " [7.7 3. 6.1 2.3]\n",
+ " [6.3 3.4 5.6 2.4]\n",
+ " [6.4 3.1 5.5 1.8]\n",
+ " [6. 3. 4.8 1.8]\n",
+ " [6.9 3.1 5.4 2.1]\n",
+ " [6.7 3.1 5.6 2.4]\n",
+ " [6.9 3.1 5.1 2.3]\n",
+ " [5.8 2.7 5.1 1.9]\n",
+ " [6.8 3.2 5.9 2.3]\n",
+ " [6.7 3.3 5.7 2.5]\n",
+ " [6.7 3. 5.2 2.3]\n",
+ " [6.3 2.5 5. 1.9]\n",
+ " [6.5 3. 5.2 2. ]\n",
+ " [6.2 3.4 5.4 2.3]\n",
+ " [5.9 3. 5.1 1.8]]\n",
+ "[0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0\n",
+ " 0 0 0 0 0 0 0 0 0 0 0 0 0 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1\n",
+ " 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 2 2 2 2 2 2 2 2 2 2 2\n",
+ " 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2\n",
+ " 2 2]\n",
+ "(150, 4)\n"
+ ]
+ }
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {
+ "id": "TZJDaTW3Or3X"
+ },
+ "source": [
+ "### *Segregate Dataset into X(Input/IndependentVariable) & Y(Output/DependentVariable)*"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "metadata": {
+ "colab": {
+ "base_uri": "https://localhost:8080/",
+ "height": 424
+ },
+ "id": "EG1_3L0hOoq0",
+ "outputId": "4497898b-6264-4286-abb3-6394ec005754"
+ },
+ "source": [
+ "X = pd.DataFrame(dataset.data, columns=dataset.feature_names)\n",
+ "X"
+ ],
+ "execution_count": 4,
+ "outputs": [
+ {
+ "output_type": "execute_result",
+ "data": {
+ "text/plain": [
+ " sepal length (cm) sepal width (cm) petal length (cm) petal width (cm)\n",
+ "0 5.1 3.5 1.4 0.2\n",
+ "1 4.9 3.0 1.4 0.2\n",
+ "2 4.7 3.2 1.3 0.2\n",
+ "3 4.6 3.1 1.5 0.2\n",
+ "4 5.0 3.6 1.4 0.2\n",
+ ".. ... ... ... ...\n",
+ "145 6.7 3.0 5.2 2.3\n",
+ "146 6.3 2.5 5.0 1.9\n",
+ "147 6.5 3.0 5.2 2.0\n",
+ "148 6.2 3.4 5.4 2.3\n",
+ "149 5.9 3.0 5.1 1.8\n",
+ "\n",
+ "[150 rows x 4 columns]"
+ ],
+ "text/html": [
+ "\n",
+ " | \n", + " | sepal length (cm) | \n", + "sepal width (cm) | \n", + "petal length (cm) | \n", + "petal width (cm) | \n", + "
|---|---|---|---|---|
| 0 | \n", + "5.1 | \n", + "3.5 | \n", + "1.4 | \n", + "0.2 | \n", + "
| 1 | \n", + "4.9 | \n", + "3.0 | \n", + "1.4 | \n", + "0.2 | \n", + "
| 2 | \n", + "4.7 | \n", + "3.2 | \n", + "1.3 | \n", + "0.2 | \n", + "
| 3 | \n", + "4.6 | \n", + "3.1 | \n", + "1.5 | \n", + "0.2 | \n", + "
| 4 | \n", + "5.0 | \n", + "3.6 | \n", + "1.4 | \n", + "0.2 | \n", + "
| ... | \n", + "... | \n", + "... | \n", + "... | \n", + "... | \n", + "
| 145 | \n", + "6.7 | \n", + "3.0 | \n", + "5.2 | \n", + "2.3 | \n", + "
| 146 | \n", + "6.3 | \n", + "2.5 | \n", + "5.0 | \n", + "1.9 | \n", + "
| 147 | \n", + "6.5 | \n", + "3.0 | \n", + "5.2 | \n", + "2.0 | \n", + "
| 148 | \n", + "6.2 | \n", + "3.4 | \n", + "5.4 | \n", + "2.3 | \n", + "
| 149 | \n", + "5.9 | \n", + "3.0 | \n", + "5.1 | \n", + "1.8 | \n", + "
150 rows × 4 columns
\n", + "DecisionTreeClassifier(criterion='entropy', max_depth=3, random_state=0)In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook.
DecisionTreeClassifier(criterion='entropy', max_depth=3, random_state=0)
"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {
+ "id": "6pk_INoSKj1N"
+ },
+ "source": [
+ "# Day-4 | Salary Estimation | K-NEAREST NEIGHBOUR model"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {
+ "id": "7OGtDD8zKwUz"
+ },
+ "source": [
+ "### *Importing Libraries*"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "metadata": {
+ "id": "7YBAsJmVKc_B"
+ },
+ "source": [
+ "import pandas as pd #useful for loading the dataset\n",
+ "import numpy as np #to perform array"
+ ],
+ "execution_count": 1,
+ "outputs": []
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {
+ "id": "rVLDLs-lK3xo"
+ },
+ "source": [
+ "### *Choose Dataset from Local Directory*"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "metadata": {
+ "id": "XA2ITub0K9k2",
+ "colab": {
+ "base_uri": "https://localhost:8080/",
+ "height": 73
+ },
+ "outputId": "02acd90f-0908-4b97-85b4-bf701901c3f6"
+ },
+ "source": [
+ "from google.colab import files\n",
+ "uploaded = files.upload()"
+ ],
+ "execution_count": 2,
+ "outputs": [
+ {
+ "output_type": "display_data",
+ "data": {
+ "text/plain": [
+ "KNeighborsClassifier(n_neighbors=2)In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook.
KNeighborsClassifier(n_neighbors=2)
"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {
+ "id": "Qmi36D7ZPY-M"
+ },
+ "source": [
+ "# **Day - 6 Titanic Survival Prediction using NAIVE BAYES**"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {
+ "id": "Q8lgHC2zPTE4"
+ },
+ "source": [
+ "### *Importing basic Libraries*"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "metadata": {
+ "id": "nKKbpfywIqAq"
+ },
+ "source": [
+ "import pandas as pd\n",
+ "import numpy as np"
+ ],
+ "execution_count": 1,
+ "outputs": []
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {
+ "id": "xfyZYdDaPnJz"
+ },
+ "source": [
+ "### *Choose Dataset file from Local Directory*"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "metadata": {
+ "colab": {
+ "base_uri": "https://localhost:8080/",
+ "height": 73
+ },
+ "id": "ki0LIHaOP869",
+ "outputId": "b9ecc9aa-e308-4031-a096-fd77b3f36d4c"
+ },
+ "source": [
+ "from google.colab import files\n",
+ "uploaded = files.upload()"
+ ],
+ "execution_count": 2,
+ "outputs": [
+ {
+ "output_type": "display_data",
+ "data": {
+ "text/plain": [
+ "| \n", + " | Pclass | \n", + "Sex | \n", + "Age | \n", + "Fare | \n", + "
|---|---|---|---|---|
| 0 | \n", + "3 | \n", + "1 | \n", + "22.0 | \n", + "7.2500 | \n", + "
| 1 | \n", + "1 | \n", + "0 | \n", + "38.0 | \n", + "71.2833 | \n", + "
| 2 | \n", + "3 | \n", + "0 | \n", + "26.0 | \n", + "7.9250 | \n", + "
| 3 | \n", + "1 | \n", + "0 | \n", + "35.0 | \n", + "53.1000 | \n", + "
| 4 | \n", + "3 | \n", + "1 | \n", + "35.0 | \n", + "8.0500 | \n", + "
| ... | \n", + "... | \n", + "... | \n", + "... | \n", + "... | \n", + "
| 886 | \n", + "2 | \n", + "1 | \n", + "27.0 | \n", + "13.0000 | \n", + "
| 887 | \n", + "1 | \n", + "0 | \n", + "19.0 | \n", + "30.0000 | \n", + "
| 888 | \n", + "3 | \n", + "0 | \n", + "NaN | \n", + "23.4500 | \n", + "
| 889 | \n", + "1 | \n", + "1 | \n", + "26.0 | \n", + "30.0000 | \n", + "
| 890 | \n", + "3 | \n", + "1 | \n", + "32.0 | \n", + "7.7500 | \n", + "
891 rows × 4 columns
\n", + "| \n", + " | Survived | \n", + "
|---|---|
| 0 | \n", + "0 | \n", + "
| 1 | \n", + "1 | \n", + "
| 2 | \n", + "1 | \n", + "
| 3 | \n", + "1 | \n", + "
| 4 | \n", + "0 | \n", + "
| ... | \n", + "... | \n", + "
| 886 | \n", + "0 | \n", + "
| 887 | \n", + "1 | \n", + "
| 888 | \n", + "0 | \n", + "
| 889 | \n", + "1 | \n", + "
| 890 | \n", + "0 | \n", + "
891 rows × 1 columns
\n", + "GaussianNB()In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook.
GaussianNB()
"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {
+ "id": "iU_l3SQ9A65p"
+ },
+ "outputs": [],
+ "source": []
+ },
+ {
+ "source": [
+ "#23_MarketBasketAnalysisusingAPIRIORI\n",
+ "### Importing the basic libraries\n",
+ "import numpy as np\n",
+ "import pandas as pd\n",
+ "import matplotlib.pyplot as plt\n",
+ "### Load Dataset from Local Directory\n",
+ "from google.colab import files\n",
+ "uploaded = files.upload()\n",
+ "### Importing the dataset\n",
+ "dataset = pd.read_csv('dataset.csv')\n",
+ "print(dataset.shape)\n",
+ "print(dataset.head(5))\n",
+ "### Data Pre-Processing using vectorized operations\n",
+ "# Extract the relevant columns (assuming first 20)\n",
+ "transactions_df = dataset.iloc[:, :20]\n",
+ "# Convert all values to strings\n",
+ "transactions_df = transactions_df.astype(str)\n",
+ "# Convert DataFrame to list of lists\n",
+ "transactions = transactions_df.values.tolist()\n",
+ "### Training APRIORI\n",
+ "!pip install apyori\n",
+ "from apyori import apriori\n",
+ "rules = apriori(transactions = transactions, min_support = 0.003, min_confidence = 0.2, min_lift = 3, min_length = 2, max_length = 2)\n",
+ "### Result\n",
+ "results = list(rules)\n",
+ "results\n",
+ "### Results in Dataframe\n",
+ "lhs = [tuple(result[2][0][0])[0] for result in results]\n",
+ "rhs = [tuple(result[2][0][1])[0] for result in results]\n",
+ "supports = [result[1] for result in results]\n",
+ "confidences = [result[2][0][2] for result in results]\n",
+ "lifts = [result[2][0][3] for result in results]\n",
+ "resultsinDataFrame = pd.DataFrame(zip(lhs, rhs, supports, confidences, lifts), columns = ['Left Hand Side', 'Right Hand Side', 'Support', 'Confidence', 'Lift'])\n",
+ "resultsinDataFrame"
+ ],
+ "cell_type": "code",
+ "metadata": {
+ "id": "lFqrYPxIAk31",
+ "colab": {
+ "base_uri": "https://localhost:8080/",
+ "height": 1291
+ },
+ "outputId": "dfb99183-e856-45ba-8a3d-9d4e58d67a90"
+ },
+ "execution_count": 1,
+ "outputs": [
+ {
+ "output_type": "display_data",
+ "data": {
+ "text/plain": [
+ "| \n", + " | Left Hand Side | \n", + "Right Hand Side | \n", + "Support | \n", + "Confidence | \n", + "Lift | \n", + "
|---|---|---|---|---|---|
| 0 | \n", + "light cream | \n", + "chicken | \n", + "0.004533 | \n", + "0.290598 | \n", + "4.843305 | \n", + "
| 1 | \n", + "mushroom cream sauce | \n", + "escalope | \n", + "0.005733 | \n", + "0.300699 | \n", + "3.790327 | \n", + "
| 2 | \n", + "pasta | \n", + "escalope | \n", + "0.005867 | \n", + "0.372881 | \n", + "4.700185 | \n", + "
| 3 | \n", + "fromage blanc | \n", + "honey | \n", + "0.003333 | \n", + "0.245098 | \n", + "5.178128 | \n", + "
| 4 | \n", + "herb & pepper | \n", + "ground beef | \n", + "0.016000 | \n", + "0.323450 | \n", + "3.291555 | \n", + "
| 5 | \n", + "tomato sauce | \n", + "ground beef | \n", + "0.005333 | \n", + "0.377358 | \n", + "3.840147 | \n", + "
| 6 | \n", + "light cream | \n", + "olive oil | \n", + "0.003200 | \n", + "0.205128 | \n", + "3.120612 | \n", + "
| 7 | \n", + "whole wheat pasta | \n", + "olive oil | \n", + "0.008000 | \n", + "0.271493 | \n", + "4.130221 | \n", + "
| 8 | \n", + "pasta | \n", + "shrimp | \n", + "0.005067 | \n", + "0.322034 | \n", + "4.514494 | \n", + "
"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {
+ "id": "AhpeX5Dm_eLW"
+ },
+ "source": [
+ "# **Day-10_BreastCancerDetection_VariousMLAlgorithm**"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {
+ "id": "8rvHswu2td0Z"
+ },
+ "source": [
+ "### *Importing Libraries*"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "metadata": {
+ "id": "P-YXxKhu_Uk4"
+ },
+ "source": [
+ "import pandas as pd #useful for loading the dataset\n",
+ "import numpy as np #to perform array\n",
+ "from matplotlib import pyplot"
+ ],
+ "execution_count": 1,
+ "outputs": []
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {
+ "id": "mOt3nocbwvZk"
+ },
+ "source": [
+ "### *Choose Dataset from Local Directory*"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "metadata": {
+ "id": "xB2NMwXtw2dG",
+ "colab": {
+ "base_uri": "https://localhost:8080/",
+ "height": 73
+ },
+ "outputId": "ec354929-3ba9-42a1-b9bb-ccf832a4bb1b"
+ },
+ "source": [
+ "from google.colab import files\n",
+ "uploaded = files.upload()"
+ ],
+ "execution_count": 2,
+ "outputs": [
+ {
+ "output_type": "display_data",
+ "data": {
+ "text/plain": [
+ ""
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 6,
+ "metadata": {
+ "colab": {
+ "base_uri": "https://localhost:8080/",
+ "height": 614
+ },
+ "id": "G2vIOdeGG43G",
+ "outputId": "689ccdcc-3ca2-4b1f-e21d-702aa480ff99"
+ },
+ "outputs": [
+ {
+ "output_type": "stream",
+ "name": "stdout",
+ "text": [
+ "Analysis complete.\n"
+ ]
+ }
+ ],
+ "source": [
+ "# prompt: create visual dashboard for speed,power,mean velocity,horsepower persecond analysis using rpm for atheletes speed analysis device where we calculate athelets speed through revolution per minute through absolute encoder sensor live.the dashboard should be more visual and should use 2d modeling for graphs of all above parameter and clearity in all above graphs ploted.also build an python application for the same .write backend effective and correctly matching front end parameter\n",
+ "\n",
+ "import matplotlib.pyplot as plt\n",
+ "import numpy as np\n",
+ "import time\n",
+ "import random\n",
+ "import pandas as pd\n",
+ "from IPython.display import clear_output\n",
+ "\n",
+ "class AthleteAnalyzer:\n",
+ " def __init__(self):\n",
+ " self.rpm_data = []\n",
+ " self.time_data = []\n",
+ " self.power_data = []\n",
+ " self.speed_data = []\n",
+ " self.mean_velocity_data = []\n",
+ " self.horsepower_per_second_data = []\n",
+ " self.start_time = time.time()\n",
+ "\n",
+ " def calculate_speed(self, rpm, radius=0.3): # Radius in meters (adjust as needed)\n",
+ " # Calculate linear speed (m/s) from RPM and radius\n",
+ " circumference = 2 * np.pi * radius\n",
+ " speed_mps = (rpm / 60) * circumference\n",
+ " return speed_mps\n",
+ "\n",
+ " def calculate_power(self, rpm, torque=10): # Torque in Nm (adjust as needed)\n",
+ " # Calculate power (watts) from RPM and torque\n",
+ " power_watts = (rpm * 2 * np.pi * torque) / 60\n",
+ " return power_watts\n",
+ "\n",
+ " def calculate_mean_velocity(self,speed_data):\n",
+ " if len(speed_data)==0:\n",
+ " return 0\n",
+ " mean_velocity = sum(speed_data) / len(speed_data)\n",
+ " return mean_velocity\n",
+ "\n",
+ " def calculate_horsepower_per_second(self,power_data):\n",
+ " if len(power_data)==0:\n",
+ " return 0\n",
+ "\n",
+ " horsepower_per_second = (power_data[-1] * 0.001341)\n",
+ " return horsepower_per_second\n",
+ "\n",
+ " def update_data(self, rpm):\n",
+ " current_time = time.time() - self.start_time\n",
+ " self.time_data.append(current_time)\n",
+ " self.rpm_data.append(rpm)\n",
+ "\n",
+ " speed = self.calculate_speed(rpm)\n",
+ " self.speed_data.append(speed)\n",
+ "\n",
+ " power = self.calculate_power(rpm)\n",
+ " self.power_data.append(power)\n",
+ "\n",
+ " mean_velocity=self.calculate_mean_velocity(self.speed_data)\n",
+ " self.mean_velocity_data.append(mean_velocity)\n",
+ "\n",
+ " horsepower_per_second=self.calculate_horsepower_per_second(self.power_data)\n",
+ " self.horsepower_per_second_data.append(horsepower_per_second)\n",
+ "\n",
+ " def create_dashboard(self):\n",
+ "\n",
+ " # Create figure and axes\n",
+ " fig, axs = plt.subplots(3, 2, figsize=(18, 12))\n",
+ "\n",
+ " # RPM Graph\n",
+ " axs[0, 0].plot(self.time_data, self.rpm_data, label=\"RPM\", color=\"blue\")\n",
+ " axs[0, 0].set_xlabel(\"Time (s)\")\n",
+ " axs[0, 0].set_ylabel(\"RPM\")\n",
+ " axs[0, 0].set_title(\"RPM Over Time\")\n",
+ " axs[0, 0].grid(True)\n",
+ "\n",
+ " # Speed Graph\n",
+ " axs[0, 1].plot(self.time_data, self.speed_data, label=\"Speed (m/s)\", color=\"green\")\n",
+ " axs[0, 1].set_xlabel(\"Time (s)\")\n",
+ " axs[0, 1].set_ylabel(\"Speed (m/s)\")\n",
+ " axs[0, 1].set_title(\"Speed Over Time\")\n",
+ " axs[0, 1].grid(True)\n",
+ "\n",
+ " # Power Graph\n",
+ " axs[1, 0].plot(self.time_data, self.power_data, label=\"Power (watts)\", color=\"red\")\n",
+ " axs[1, 0].set_xlabel(\"Time (s)\")\n",
+ " axs[1, 0].set_ylabel(\"Power (watts)\")\n",
+ " axs[1, 0].set_title(\"Power Over Time\")\n",
+ " axs[1, 0].grid(True)\n",
+ "\n",
+ " #Mean Velocity Graph\n",
+ " axs[1, 1].plot(self.time_data, self.mean_velocity_data, label=\"Mean Velocity\", color=\"orange\")\n",
+ " axs[1, 1].set_xlabel(\"Time (s)\")\n",
+ " axs[1, 1].set_ylabel(\"Mean Velocity\")\n",
+ " axs[1, 1].set_title(\"Mean Velocity Over Time\")\n",
+ " axs[1, 1].grid(True)\n",
+ "\n",
+ " #Horse power per second Graph\n",
+ " axs[2, 0].plot(self.time_data, self.horsepower_per_second_data, label=\"Horse Power Per Second\", color=\"purple\")\n",
+ " axs[2, 0].set_xlabel(\"Time (s)\")\n",
+ " axs[2, 0].set_ylabel(\"Horsepower/s\")\n",
+ " axs[2, 0].set_title(\"Horse Power per Second Over Time\")\n",
+ " axs[2, 0].grid(True)\n",
+ "\n",
+ " fig.delaxes(axs[2,1])\n",
+ "\n",
+ " plt.tight_layout()\n",
+ " plt.show()\n",
+ "\n",
+ " def run_analysis(self, duration=30, update_interval=1):\n",
+ " end_time = time.time() + duration\n",
+ " while time.time() < end_time:\n",
+ " # Simulate RPM data from sensor (replace with actual sensor reading)\n",
+ " rpm = random.randint(50, 150)\n",
+ " self.update_data(rpm)\n",
+ " self.create_dashboard()\n",
+ "\n",
+ " # Clear the output and update the dashboard\n",
+ " clear_output(wait=True)\n",
+ "\n",
+ " time.sleep(update_interval)\n",
+ " print(\"Analysis complete.\")\n",
+ "\n",
+ "if __name__ == \"__main__\":\n",
+ " analyzer = AthleteAnalyzer()\n",
+ " analyzer.run_analysis()\n",
+ "\n"
+ ]
+ }
+ ]
+}
\ No newline at end of file
diff --git a/breastcancer_6.ipynb b/breastcancer_6.ipynb
new file mode 100644
index 0000000..e099796
--- /dev/null
+++ b/breastcancer_6.ipynb
@@ -0,0 +1,835 @@
+{
+ "nbformat": 4,
+ "nbformat_minor": 0,
+ "metadata": {
+ "colab": {
+ "provenance": [],
+ "authorship_tag": "ABX9TyPEBJ6UnJGiLAl/F4VbQEn/",
+ "include_colab_link": true
+ },
+ "kernelspec": {
+ "name": "python3",
+ "display_name": "Python 3"
+ },
+ "language_info": {
+ "name": "python"
+ }
+ },
+ "cells": [
+ {
+ "cell_type": "markdown",
+ "metadata": {
+ "id": "view-in-github",
+ "colab_type": "text"
+ },
+ "source": [
+ ""
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {
+ "id": "4BYhNI_DVlrd"
+ },
+ "outputs": [],
+ "source": []
+ },
+ {
+ "source": [
+ "from IPython import get_ipython\n",
+ "from IPython.display import display\n",
+ "import numpy as np\n",
+ "import pandas as pd\n",
+ "import matplotlib.pyplot as plt\n",
+ "from sklearn.model_selection import train_test_split\n",
+ "from google.colab import files\n",
+ "from sklearn.preprocessing import LabelEncoder\n",
+ "from xgboost import XGBClassifier\n",
+ "from sklearn.metrics import confusion_matrix, accuracy_score\n",
+ "from sklearn.model_selection import cross_val_score"
+ ],
+ "cell_type": "code",
+ "metadata": {
+ "id": "x58skzkAVrK9"
+ },
+ "execution_count": 1,
+ "outputs": []
+ },
+ {
+ "source": [
+ "# Load Dataset from Local Directory\n",
+ "uploaded = files.upload()\n",
+ "\n",
+ "# Importing the dataset\n",
+ "dataset = pd.read_csv('dataset.csv')\n",
+ "print(dataset.shape)\n",
+ "print(dataset.head(5))"
+ ],
+ "cell_type": "code",
+ "metadata": {
+ "colab": {
+ "base_uri": "https://localhost:8080/",
+ "height": 460
+ },
+ "id": "Wv58GcVQVsAP",
+ "outputId": "1c7e07ef-7408-4d02-fcc4-54546265d3e3"
+ },
+ "execution_count": 2,
+ "outputs": [
+ {
+ "output_type": "display_data",
+ "data": {
+ "text/plain": [
+ "XGBClassifier(base_score=None, booster=None, callbacks=None,\n", + " colsample_bylevel=None, colsample_bynode=None,\n", + " colsample_bytree=None, device=None, early_stopping_rounds=None,\n", + " enable_categorical=False, eval_metric=None, feature_types=None,\n", + " gamma=None, grow_policy=None, importance_type=None,\n", + " interaction_constraints=None, learning_rate=None, max_bin=None,\n", + " max_cat_threshold=None, max_cat_to_onehot=None,\n", + " max_delta_step=None, max_depth=None, max_leaves=None,\n", + " min_child_weight=None, missing=nan, monotone_constraints=None,\n", + " multi_strategy=None, n_estimators=None, n_jobs=None,\n", + " num_parallel_tree=None, random_state=None, ...)In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook.
XGBClassifier(base_score=None, booster=None, callbacks=None,\n", + " colsample_bylevel=None, colsample_bynode=None,\n", + " colsample_bytree=None, device=None, early_stopping_rounds=None,\n", + " enable_categorical=False, eval_metric=None, feature_types=None,\n", + " gamma=None, grow_policy=None, importance_type=None,\n", + " interaction_constraints=None, learning_rate=None, max_bin=None,\n", + " max_cat_threshold=None, max_cat_to_onehot=None,\n", + " max_delta_step=None, max_depth=None, max_leaves=None,\n", + " min_child_weight=None, missing=nan, monotone_constraints=None,\n", + " multi_strategy=None, n_estimators=None, n_jobs=None,\n", + " num_parallel_tree=None, random_state=None, ...)