diff --git a/Digital-Clock/Digital-Clock.py b/scripts/Digital-Clock/Digital-Clock.py similarity index 100% rename from Digital-Clock/Digital-Clock.py rename to scripts/Digital-Clock/Digital-Clock.py diff --git a/Digital-Clock/readme.md b/scripts/Digital-Clock/readme.md similarity index 100% rename from Digital-Clock/readme.md rename to scripts/Digital-Clock/readme.md diff --git a/Projects/DigitRecognitionusingRandomForestClassifier.ipynb b/scripts/RandomForest Classifier/DigitRecognitionusingRandomForestClassifier.ipynb similarity index 96% rename from Projects/DigitRecognitionusingRandomForestClassifier.ipynb rename to scripts/RandomForest Classifier/DigitRecognitionusingRandomForestClassifier.ipynb index b440c98..6014f83 100644 --- a/Projects/DigitRecognitionusingRandomForestClassifier.ipynb +++ b/scripts/RandomForest Classifier/DigitRecognitionusingRandomForestClassifier.ipynb @@ -3,8 +3,8 @@ { "cell_type": "markdown", "metadata": { - "id": "view-in-github", - "colab_type": "text" + "colab_type": "text", + "id": "view-in-github" }, "source": [ "\"Open" @@ -62,11 +62,7 @@ }, "outputs": [ { - "output_type": "display_data", "data": { - "text/plain": [ - "" - ], "text/html": [ "\n", " " + ], + "text/plain": [ + "" ] }, - "metadata": {} + "metadata": {}, + "output_type": "display_data" }, { - "output_type": "stream", "name": "stdout", + "output_type": "stream", "text": [ "Saving train[1].csv to train[1].csv\n" ] @@ -270,39 +270,36 @@ }, { "cell_type": "markdown", - "source": [ - "**Load Dataset**" - ], "metadata": { "id": "TJRApm0w0Dct" - } + }, + "source": [ + "**Load Dataset**" + ] }, { "cell_type": "code", - "source": [ - "dataset = pd.read_csv('train.csv')" - ], + "execution_count": 4, "metadata": { "id": "GyOvJOoR0Lhq" }, - "execution_count": 4, - "outputs": [] + "outputs": [], + "source": [ + "dataset = pd.read_csv('train.csv')" + ] }, { "cell_type": "markdown", - "source": [ - "**Summarize dataset**" - ], "metadata": { "id": "0txmydWY0ZEH" - } + }, + "source": [ + "**Summarize dataset**" + ] }, { "cell_type": "code", - "source": [ - "print(dataset.shape)\n", - "print(dataset.head(5))" - ], + "execution_count": 5, "metadata": { "colab": { "base_uri": "https://localhost:8080/" @@ -310,11 +307,10 @@ "id": "AW-9ITV10cIY", "outputId": "dce2cb6d-2bdb-41e5-de9e-baf122900140" }, - "execution_count": 5, "outputs": [ { - "output_type": "stream", "name": "stdout", + "output_type": "stream", "text": [ "(42000, 785)\n", " label pixel0 pixel1 pixel2 pixel3 pixel4 pixel5 pixel6 pixel7 \\\n", @@ -341,24 +337,24 @@ "[5 rows x 785 columns]\n" ] } + ], + "source": [ + "print(dataset.shape)\n", + "print(dataset.head(5))" ] }, { "cell_type": "markdown", - "source": [ - "**Segregate Dataset into X(Input/Independent Variable) & Y(Output/Dependent Variable)**" - ], "metadata": { "id": "QUh5BKq20viv" - } + }, + "source": [ + "**Segregate Dataset into X(Input/Independent Variable) & Y(Output/Dependent Variable)**" + ] }, { "cell_type": "code", - "source": [ - "X = dataset.iloc[:,1:]\n", - "print(X)\n", - "print(X.shape)" - ], + "execution_count": 6, "metadata": { "colab": { "base_uri": "https://localhost:8080/" @@ -366,11 +362,10 @@ "id": "OP2TX3iX09ND", "outputId": "9c8f44e2-a503-4acf-8978-f6576706e402" }, - "execution_count": 6, "outputs": [ { - "output_type": "stream", "name": "stdout", + "output_type": "stream", "text": [ " pixel0 pixel1 pixel2 pixel3 pixel4 pixel5 pixel6 pixel7 pixel8 \\\n", "0 0 0 0 0 0 0 0 0 0 \n", @@ -415,15 +410,16 @@ "(42000, 784)\n" ] } + ], + "source": [ + "X = dataset.iloc[:,1:]\n", + "print(X)\n", + "print(X.shape)" ] }, { "cell_type": "code", - "source": [ - "Y = dataset.iloc[:,0]\n", - "print(Y)\n", - "print(Y.shape)" - ], + "execution_count": 7, "metadata": { "colab": { "base_uri": "https://localhost:8080/" @@ -431,11 +427,10 @@ "id": "2RuBl7671GH4", "outputId": "96d6afef-f2ed-420f-d95c-826a287fa8dd" }, - "execution_count": 7, "outputs": [ { - "output_type": "stream", "name": "stdout", + "output_type": "stream", "text": [ "0 1\n", "1 0\n", @@ -452,45 +447,46 @@ "(42000,)\n" ] } + ], + "source": [ + "Y = dataset.iloc[:,0]\n", + "print(Y)\n", + "print(Y.shape)" ] }, { "cell_type": "markdown", - "source": [ - "**Splitting Dataset into Test and Train**" - ], "metadata": { "id": "o1j-AGZd1OQV" - } + }, + "source": [ + "**Splitting Dataset into Test and Train**" + ] }, { "cell_type": "code", - "source": [ - "from sklearn.model_selection import train_test_split\n", - "X_train, X_test, y_train, y_test = train_test_split(X,Y, test_size = 0.25, random_state = 0)" - ], + "execution_count": 8, "metadata": { "id": "U_c_R4HA1SeZ" }, - "execution_count": 8, - "outputs": [] + "outputs": [], + "source": [ + "from sklearn.model_selection import train_test_split\n", + "X_train, X_test, y_train, y_test = train_test_split(X,Y, test_size = 0.25, random_state = 0)" + ] }, { "cell_type": "markdown", - "source": [ - "**Training**" - ], "metadata": { "id": "Gf6EgvAc1vjh" - } + }, + "source": [ + "**Training**" + ] }, { "cell_type": "code", - "source": [ - "from sklearn.ensemble import RandomForestClassifier\n", - "model = RandomForestClassifier()\n", - "model.fit(X_train, y_train)" - ], + "execution_count": 9, "metadata": { "colab": { "base_uri": "https://localhost:8080/" @@ -498,46 +494,47 @@ "id": "RS4TAnDh1yUU", "outputId": "4803259d-f3a1-461f-d3d0-939bc4495a64" }, - "execution_count": 9, "outputs": [ { - "output_type": "execute_result", "data": { "text/plain": [ "RandomForestClassifier()" ] }, + "execution_count": 9, "metadata": {}, - "execution_count": 9 + "output_type": "execute_result" } + ], + "source": [ + "from sklearn.ensemble import RandomForestClassifier\n", + "model = RandomForestClassifier()\n", + "model.fit(X_train, y_train)" ] }, { "cell_type": "code", - "source": [ - "y_pred = model.predict(X_test)" - ], + "execution_count": 10, "metadata": { "id": "SljeEEbs2JFT" }, - "execution_count": 10, - "outputs": [] + "outputs": [], + "source": [ + "y_pred = model.predict(X_test)" + ] }, { "cell_type": "markdown", - "source": [ - "**Model Accuracy**" - ], "metadata": { "id": "4XEvHILm2OF-" - } + }, + "source": [ + "**Model Accuracy**" + ] }, { "cell_type": "code", - "source": [ - "from sklearn.metrics import accuracy_score\n", - "print(\"Accuracy of the Model: {0}%\".format(accuracy_score(y_test, y_pred)*100))" - ], + "execution_count": 11, "metadata": { "colab": { "base_uri": "https://localhost:8080/" @@ -545,26 +542,23 @@ "id": "sHEVc1Qq2Rqy", "outputId": "06be6e32-1ba4-4035-eafb-3b3c2023abd6" }, - "execution_count": 11, "outputs": [ { - "output_type": "stream", "name": "stdout", + "output_type": "stream", "text": [ "Accuracy of the Model: 96.31428571428572%\n" ] } + ], + "source": [ + "from sklearn.metrics import accuracy_score\n", + "print(\"Accuracy of the Model: {0}%\".format(accuracy_score(y_test, y_pred)*100))" ] }, { "cell_type": "code", - "source": [ - "import matplotlib.pyplot as plt\n", - "index=10\n", - "print(\"Predicted \" +str(model.predict(X_test)[index]))\n", - "plt.axis('off')\n", - "plt.imshow(X_test.iloc[index].values.reshape((28,28)),cmap='gray')" - ], + "execution_count": 13, "metadata": { "colab": { "base_uri": "https://localhost:8080/", @@ -573,54 +567,67 @@ "id": "iymJ1Zpj20gk", "outputId": "ae21ce24-b957-4a30-8f04-ec5c77dd5a53" }, - "execution_count": 13, "outputs": [ { - "output_type": "stream", "name": "stdout", + "output_type": "stream", "text": [ "Predicted 7\n" ] }, { - "output_type": "execute_result", "data": { "text/plain": [ "" ] }, + "execution_count": 13, "metadata": {}, - "execution_count": 13 + "output_type": "execute_result" }, { - "output_type": "display_data", "data": { + "image/png": "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", "text/plain": [ "
" - ], - "image/png": "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\n" + ] }, "metadata": { "needs_background": "light" - } + }, + "output_type": "display_data" } + ], + "source": [ + "import matplotlib.pyplot as plt\n", + "index=10\n", + "print(\"Predicted \" +str(model.predict(X_test)[index]))\n", + "plt.axis('off')\n", + "plt.imshow(X_test.iloc[index].values.reshape((28,28)),cmap='gray')" ] } ], "metadata": { "colab": { - "provenance": [], "authorship_tag": "ABX9TyOBzEe2vR1rQh4B8yWT0mhr", - "include_colab_link": true + "include_colab_link": true, + "provenance": [] }, "kernelspec": { - "display_name": "Python 3", + "display_name": "Python 3.8.9 64-bit", + "language": "python", "name": "python3" }, "language_info": { - "name": "python" + "name": "python", + "version": "3.8.9" + }, + "vscode": { + "interpreter": { + "hash": "31f2aee4e71d21fbe5cf8b01ff0e069b9275f58929596ceb00d14d90e3e16cd6" + } } }, "nbformat": 4, "nbformat_minor": 0 -} \ No newline at end of file +} diff --git a/Projects/Sentiment Analysis/Restaurant_Reviews.tsv b/scripts/Sentiment Analysis/Restaurant_Reviews.tsv similarity index 100% rename from Projects/Sentiment Analysis/Restaurant_Reviews.tsv rename to scripts/Sentiment Analysis/Restaurant_Reviews.tsv diff --git a/Projects/Sentiment Analysis/senti.py b/scripts/Sentiment Analysis/senti.py similarity index 100% rename from Projects/Sentiment Analysis/senti.py rename to scripts/Sentiment Analysis/senti.py