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@syamkakarla98
syamkakarla98 committed Feb 13, 2019
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1,201 changes: 1,201 additions & 0 deletions CSV_datasetsix_vowel_dataset_with_class.csv
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910 changes: 910 additions & 0 deletions NoteBooks/.ipynb_checkpoints/Data_Analysis-checkpoint.ipynb
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537 changes: 537 additions & 0 deletions NoteBooks/.ipynb_checkpoints/KNN Classifier on Dataset-checkpoint.ipynb
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618 changes: 618 additions & 0 deletions NoteBooks/.ipynb_checkpoints/Neural Network on Handwritten Digits-checkpoint.ipynb
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539 changes: 539 additions & 0 deletions NoteBooks/.ipynb_checkpoints/PCA on Dataset-checkpoint.ipynb
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226 changes: 226 additions & 0 deletions NoteBooks/.ipynb_checkpoints/Primary Data Analysis-checkpoint.ipynb
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364 changes: 364 additions & 0 deletions NoteBooks/.ipynb_checkpoints/SVM on HandWritten Dataset-checkpoint.ipynb
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{
"cells": [
{
"cell_type": "markdown",
"metadata": {},
"source": [
"# SVM on Handwritten Digits"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Loading libraries"
]
},
{
"cell_type": "code",
"execution_count": 1,
"metadata": {},
"outputs": [],
"source": [
"import matplotlib.pyplot as plt\n",
"from sklearn.svm import SVC\n",
"import numpy as np\n",
"import matplotlib.pyplot as plt\n",
"import pandas as pd\n",
"from sklearn import decomposition\n",
"from sklearn.preprocessing import StandardScaler\n",
"from sklearn.model_selection import train_test_split\n",
"from sklearn import metrics"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Loading data"
]
},
{
"cell_type": "code",
"execution_count": 2,
"metadata": {},
"outputs": [
{
"data": {
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},
"execution_count": 2,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"df = pd.read_csv(\"E://Telugu Character Recogniton//CSV_dataset//CSV_datasetsix_vowel_dataset_with_class.csv\")\n",
"df.head()"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Splitting data into 'train' and 'test'"
]
},
{
"cell_type": "code",
"execution_count": 3,
"metadata": {},
"outputs": [],
"source": [
"pix=[]\n",
"for i in range(784):\n",
" pix.append('pixel'+str(i))\n",
"features=pix\n",
"X = df.loc[:, features].values\n",
"y = df.loc[:,'class'].values\n",
"\n",
"X_train, X_test, y_train, y_test = train_test_split(\n",
" X, y, test_size = 0.25, random_state = 100)\n",
"y_train=y_train.ravel()\n",
"y_test=y_test.ravel()"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Building classifier"
]
},
{
"cell_type": "code",
"execution_count": 4,
"metadata": {},
"outputs": [],
"source": [
"\n",
"svm_model = SVC(kernel = 'poly', C = 1, gamma=2).fit(X_train, y_train)\n",
"svm_predictions = svm_model.predict(X_test)\n"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Finding accuracy of the model"
]
},
{
"cell_type": "code",
"execution_count": 5,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Accuracy: 89.33333333333333\n"
]
}
],
"source": [
"accuracy = svm_model.score(X_test, y_test)\n",
"print('Accuracy: ',accuracy*100)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## <font color='tomato'>Accuracy of the model is: </font><font color='MediumSpringGreen'>89.333333333</font>"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Conclusion:\n",
"* <font color='navy' face='lucida console'><strong>Support Vector Machine(SVM) performs well on the small dataset when compared to other models like K-NNC and Neural Neetworks.</strong></font>"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": []
}
],
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