diff --git a/src/03b.ipynb b/src/03b.ipynb index 6bb7bfe..31c6b3c 100644 --- a/src/03b.ipynb +++ b/src/03b.ipynb @@ -34,7 +34,7 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 1, "id": "de4b4a56", "metadata": {}, "outputs": [], @@ -52,7 +52,7 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 2, "id": "b53e3b3f", "metadata": {}, "outputs": [], @@ -70,10 +70,85 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 3, "id": "046f5901", "metadata": {}, - "outputs": [], + "outputs": [ + { + "data": { + "text/html": [ + "
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IncomeLoan AmountDefault
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12210No
23312No
32820No
42332No
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IncomeLoan Amount
count30.00000030.000000
mean20.96666754.233333
std6.19501128.231412
min12.0000008.000000
25%16.25000032.000000
50%20.50000054.500000
75%24.75000071.750000
max34.000000110.000000
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" + ], + "text/plain": [ + " Income Loan Amount\n", + "count 30.000000 30.000000\n", + "mean 20.966667 54.233333\n", + "std 6.195011 28.231412\n", + "min 12.000000 8.000000\n", + "25% 16.250000 32.000000\n", + "50% 20.500000 54.500000\n", + "75% 24.750000 71.750000\n", + "max 34.000000 110.000000" + ] + }, + "execution_count": 5, + "metadata": {}, + "output_type": "execute_result" + } + ], "source": [ "loan.describe()" ] @@ -167,7 +346,7 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 6, "id": "7239372b", "metadata": {}, "outputs": [], @@ -186,10 +365,21 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 7, "id": "9308d55a", "metadata": {}, - "outputs": [], + "outputs": [ + { + "data": { + "image/png": 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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], "source": [ "ax = sns.boxplot(data = loan, x = 'Default', y = 'Income')" ] @@ -212,10 +402,21 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 8, "id": "bcb7b490", "metadata": {}, - "outputs": [], + "outputs": [ + { + "data": { + "image/png": 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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], "source": [ "ax = sns.boxplot(data = loan, x = 'Default', y = 'Loan Amount')" ] @@ -247,7 +448,7 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 9, "id": "04b9e4bd", "metadata": {}, "outputs": [], @@ -265,10 +466,21 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 10, "id": "f0e2438f", "metadata": {}, - "outputs": [], + "outputs": [ + { + "data": { + "image/png": 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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], "source": [ "ax = sns.scatterplot(x = loan['Income'], \n", " y = np.where(loan['Default'] == 'No', 0, 1), \n", @@ -285,12 +497,23 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 11, "id": "1387c926", "metadata": { "scrolled": true }, - "outputs": [], + "outputs": [ + { + "data": { + "image/png": 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" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], "source": [ "ax = sns.scatterplot(x = loan['Loan Amount'], \n", " y = np.where(loan['Default'] == 'No', 0, 1), \n", @@ -333,13 +556,15 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 12, "id": "a570e61b", "metadata": { "scrolled": false }, "outputs": [], - "source": [] + "source": [ + "y = loan[['Default']]" + ] }, { "cell_type": "markdown", @@ -351,13 +576,15 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 13, "id": "1fda732e", "metadata": { "scrolled": false }, "outputs": [], - "source": [] + "source": [ + "x = loan[['Income','Loan Amount']]" + ] }, { "cell_type": "markdown", @@ -369,12 +596,12 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 19, "id": "cd01aaca", "metadata": {}, "outputs": [], "source": [ - "from sklearn.model_selection import train_test_split" + "from sklearn.model_selection import train_test_split\n" ] }, { @@ -395,11 +622,16 @@ }, { "cell_type": "code", - "execution_count": null, - "id": "346cdb9d", + "execution_count": 20, + "id": "21dc0800", "metadata": {}, "outputs": [], - "source": [] + "source": [ + "X_train,X_test,y_train,y_test = train_test_split(x,y,\n", + " train_size= 0.7,\n", + " stratify = y,\n", + " random_state = 1234)" + ] }, { "cell_type": "markdown", @@ -419,13 +651,26 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 18, "id": "66162c9a", "metadata": { "scrolled": false }, - "outputs": [], - "source": [] + "outputs": [ + { + "data": { + "text/plain": [ + "(9, 2)" + ] + }, + "execution_count": 18, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "X_test.shape" + ] }, { "cell_type": "markdown", @@ -439,11 +684,24 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 21, "id": "20c26601", "metadata": {}, - "outputs": [], - "source": [] + "outputs": [ + { + "data": { + "text/plain": [ + "(21, 2)" + ] + }, + "execution_count": 21, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "X_train.shape" + ] }, { "cell_type": "markdown", @@ -456,7 +714,7 @@ ], "metadata": { "kernelspec": { - "display_name": "Python 3 (ipykernel)", + "display_name": "Python 3", "language": "python", "name": "python3" }, @@ -470,7 +728,7 @@ "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", - "version": "3.9.7" + "version": "3.12.1" } }, "nbformat": 4,